This document summarizes a study that models and forecasts energy consumption in Ghana using seasonal ARIMA models. The author obtained monthly energy consumption data from 2001-2011 from Ghana's Ministry of Energy. Various SARIMA models were identified and fitted to the data. The best fitting model was selected as SARIMA (1,1,1)(0,1,2) based on having the lowest Akaike Information Criterion and Schwartz Bayesian Criterion values. This model was used to accurately forecast energy consumption for 2013 based on validation with 2012 data. The study aims to provide a modeling tool for long-term energy planning in Ghana.
ELECTRICITY CONSUMPTION AND ECONOMIC GROWTH IN SWAZILANDpaperpublications3
Abstract: The issue of causality between electricity consumption and economic growth (GDP) has been a topic concerning energy economists’ for a number of years given that the results have important implications for policy makers. This interest has been stimulated by the persistent increase in the awareness of global warming and climate change. Furthermore, this issue is currently of fundamental importance given the very real threat of global warming and hence the need to cut electricity consumption to reduce emissions to help stem climate change. Renewable energy plays a vital role in economic growth. Energy consumption is, in Africa, one of the mostly consumed capital goods for economic growth realization, and it has nowadays become a need for the society to function properly.
Explaining the relationship between energy consumption and economic growth in...Agriculture Journal IJOEAR
Abstract— The aim of this paper is to explore the energy consumption-economic growth nexus for four emerging countries (Brazil, Russia, India and China – the BRIC countries) over the period 1989-2014. By applying a set of recent panel data models, we show that increases in real per capita GDP have a positive and statistically significant effect on per capita energy consumption (and vice-versa). In the long term, a 1% increase in real per capita GDP raises the energy consumption per capita by about 0.56-0.67% while a 1% increase in per capita energy use increases the real per capita GDP by about 0,87-1.69%. Thus, the impact of real GDP on energy consumption is less important than vice versa.
Global issue based power generation expansion planning for a power systemeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
ELECTRICITY CONSUMPTION AND ECONOMIC GROWTH IN SWAZILANDpaperpublications3
Abstract: The issue of causality between electricity consumption and economic growth (GDP) has been a topic concerning energy economists’ for a number of years given that the results have important implications for policy makers. This interest has been stimulated by the persistent increase in the awareness of global warming and climate change. Furthermore, this issue is currently of fundamental importance given the very real threat of global warming and hence the need to cut electricity consumption to reduce emissions to help stem climate change. Renewable energy plays a vital role in economic growth. Energy consumption is, in Africa, one of the mostly consumed capital goods for economic growth realization, and it has nowadays become a need for the society to function properly.
Explaining the relationship between energy consumption and economic growth in...Agriculture Journal IJOEAR
Abstract— The aim of this paper is to explore the energy consumption-economic growth nexus for four emerging countries (Brazil, Russia, India and China – the BRIC countries) over the period 1989-2014. By applying a set of recent panel data models, we show that increases in real per capita GDP have a positive and statistically significant effect on per capita energy consumption (and vice-versa). In the long term, a 1% increase in real per capita GDP raises the energy consumption per capita by about 0.56-0.67% while a 1% increase in per capita energy use increases the real per capita GDP by about 0,87-1.69%. Thus, the impact of real GDP on energy consumption is less important than vice versa.
Global issue based power generation expansion planning for a power systemeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Humanities and Social Science Invention (IJHSSI)inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Analysis of Households’ Electricity Consumption with Ordered Logit Models: Ex...inventionjournals
Percentage of households’ electricity demand in total energy demand of households is increasing day by day. However, households’ electricity consumption fails to provide the added value to Gross National Product unlike industry sector. Therefore, the factors that increase the energy consumption of households should be analyzed and in this respect, required energy saving policies should be generated. In this paper, the ordered logit models examined the variables affecting the electricity consumption of households in Turkey. According to goodness of fit indicators, Partial Proportional Odds Model was determined as the best model that fits into our dataset. The results obtained from model show that electrically powered items and their quantities, household size, income, housing type and properties are important factors that increase households’ electricity consumption.
Senza sufficienza la efficienza non basta Morosini 2017morosini1952
Senza sufficienza, l’efficienza non basta. L’ effetto rebound
Marco Morosini, Politecnico federale di Zurigo, marcomorosini.eu mamo@ethz.ch
19.5.2017
------------------------------------
Effetto rebound
(paradosso dell’efficienza, paradosso di Jevons)
L’aumento dell’efficienza energetica
abbassa il prezzo dei servizi energetici
e ne aumenta la accessibilità e il consumo complessivo.
Da millenni l’aumento dell’efficienza energetica
è il principale presupposto per
l’aumento del consumo totale di energia.
----------------------
Riassunto
1. Tutte le conversioni antropiche dell’energia
hanno costi sociali e ambientali.
Nessuna tecnologia energetica può espandersi all’infinito.
2. L’efficienza è ambivalente:
- può far diminuire il consumo di energia in settori saturabili
(es. riscaldamento, climatizzazione)
- ma permette di far crescere l’uso totale di energia
(effetto rebound)
nella società nel suo complesso e in settori non saturabili
(es. trasporti privati, beni di consumo, viaggi, vacanze)
3. Un limite volontario a 2000 watt pro capite sviluppa una parsimonia energetica, che include anche la parsimonia di energie fossili
4. Una “società a 2000 watt” nei Paesi industriali
offre ai Paesi in via di sviluppo un esempio
di organizzazione sociale e di tecnologie sostenibili.
5. La sufficienza necessaria è:
- individuale: moderazione volontaria dei consumi
- collettiva: incentivi, disincentivi, prescrizioni, divieti
Conclusione
Sufficienza e efficienza devono essere sempre insieme nell’agenda culturale e in quella politica
http://www.greengrowthknowledge.org/resource/green-growth-unravelled-how-rebound-effects-baffle-sustainability-targets-when-economy
http://www.greengrowthknowledge.org/sites/default/files/downloads/resource/GG_Unravelled_HBF_and_WI.pdf
Cointegration relationship betweeCOINTEGRATION RELATIONSHIP BETWEEN ECONOMIC ...aeijjournal
Energy dependent small developing island states are besieged to sustain potential rate of growth. This is
due to increase in energy prices and lack of evidence based policy on long term sustainable energy use.
This paper examines the long run relationship between economic growth, export and electricity
consumption in Fiji over the period 1981-2011. Employing Granger causality test it is found that there is
cointegrating relationship between economic growth, export and electricity consumption. The casual
relationship between the variables was investigated within the error correction model framework. We
found that in the long run causality runs from electricity consumption and export to economic growth.
Based on this empirical analysis some important policy implications are suggested.
The changing environmental scenario and depleting conventional energy resources has impelled the world to seek out alternative sources of energy. Mankind is consciously moving towards the use of renewable energy, with the prime focus on alleviating dependency on conventional energy sources. Acting in this direction, the United Nations (UN) has started a global initiative - ‘Sustainable Energy for All’ - to drive the use of renewable energy.
The Global Survey of the Electrical Energy Distribution System: A ReviewIJECEIAES
This paper gives a review of energy scenario in India and other countries. Today’s demand of the world is to minimize greenhouse gas emissions, during the production of electricity. Henceforth over the world, the production of electrical power is changing by introducing abundantly available renewable energy sources like sun and wind. But, because of the intermittent nature of sustainable power sources, the electrical power network faces many problems, during the transmission and distribution of electricity. For resolving these issues, Electrical Energy Storage (EES) is acknowledged as supporting technology. This paper discusses about the world electrical energy scenario with top renowned developed countries in power generation and consumption. Contribution of traditional power sources changed after the introduction of renewable energy sources like sun and wind. Worldwide Agencies are formed like International Energy Agency (IEA), The Central Intelligence Agency, (CIS) etc. The main aim of these agencies is to provide reliable, affordable and clean energy. This paper will discuss about the regulatory authority and government policies/incentives taken by different countries. At the end of this paper, author focuses on obstacles in implementation, development and benefits of renewable energy.
This is the booklet that accompanies BP's Energy Outlook 2030 presentation.
We hope that sharing this outlook contributes to the wider debate on global energy issues. It identifies long-term energy trends, building on our Statistical Review of World Energy, and then develops projections for world energy markets to 2030, taking account of the potential evolution of the world economy, policy, and technology.
The outlook reflects a ‘to the best of our knowledge’ assessment of the world’s likely path from today’s vantage point, drawing on expertise both within and outside the company. It is not a statement about how we would like the market to evolve.
The outlook highlights the growing role of developing economies in global energy consumption, and the increasing share of non-fossil fuels in global energy supply. It emphasizes the importance of both improving energy efficiency and expanding energy supplies to meet the energy needs of billions of people who aspire to better lifestyles, and doing so in a way that is sustainable and secure. This year’s edition has a special focus on the role of shale gas and tight oil in supporting the growth of gas and oil supply. It also notes the uncertainties attached to any long term projection. The discipline of building a numerical projection sharpens our thinking, but the precise numbers are less important than the underlying story of the challenges we all face and the choices we make in producing and consuming energy.
For more information and to download summary tables in Excel format, please visit: http://bit.ly/BPEO2013
This presentation discusses a generalized approach to handle industrial energy use data as compared to production data, in order to predict possible savings
Presentation on Energy consumption and measures to save power in facilities operated by ACUAMED, by Gabriela Mañueco, Acuamed at International at 2014 UN-Water Annual International Zaragoza Conference. Preparing for World Water Day 2014: Partnerships for improving water and energy access, efficiency and sustainability. 13-16 January 2014.
This paper presents findings from a qualitative study of people's everyday interactions with energy-consuming products and systems in the home. Initial results from a large online survey are also considered. This research focuses not only on "conservation behavior" but importantly investigates interactions with technology that may be characterized as "normal consumption" or "over-consumption." A novel vocabulary for analyzing and designing energy-conserving interactions is proposed based on our findings, including: cutting, trimming, switching, upgrading, and shifting. Using the proposed vocabulary, and informed by theoretical developments from various literatures, this paper demonstrates ways in which everyday interactions with technology in the home are performed without conscious consideration of energy consumption but rather are unconscious, habitual, and irrational. Implications for the design of energy-conserving interactions with technology and broader challenges for HCI research are proposed.
ECMFA 2015 - Energy Consumption Analysis and Design with Foundational UMLLuca Berardinelli
Wireless Sensor Networks (WSN) are nowadays applied to a
wide set of domains (e.g., security, health). WSN are networks of spatially distributed, radio-communicating, battery-powered, autonomous sensor nodes. WSN are characterized by scarcity of resources, hence an application running on them should carefully manage its resources. The most critical resource in WSN is the nodes’ battery.
In this paper, we propose model-based engineering facilities to analyze the energy consumption and to develop energy-aware applications for WSN that are based on Agilla Middleware. For this aim i) we extend the Agilla Instruction Set with the new battery instruction able to retrieve the battery Voltage of a WSN node at run-time; ii) we measure the energy that the execution of each Agilla instruction consumes on a target platform; and iii) we extend the Agilla Modeling Framework with a new analysis that, leveraging the conducted energy consumption measurements, predicts the energy required by the Agilla agents running on the WSN. Such analysis, implemented in fUML, is based on simulation and it guides the design of WSN applications that guarantee low energy consumption. The approach is showed on the Reader agent used in the Wild Fire Tracker Application.
International Journal of Humanities and Social Science Invention (IJHSSI)inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Analysis of Households’ Electricity Consumption with Ordered Logit Models: Ex...inventionjournals
Percentage of households’ electricity demand in total energy demand of households is increasing day by day. However, households’ electricity consumption fails to provide the added value to Gross National Product unlike industry sector. Therefore, the factors that increase the energy consumption of households should be analyzed and in this respect, required energy saving policies should be generated. In this paper, the ordered logit models examined the variables affecting the electricity consumption of households in Turkey. According to goodness of fit indicators, Partial Proportional Odds Model was determined as the best model that fits into our dataset. The results obtained from model show that electrically powered items and their quantities, household size, income, housing type and properties are important factors that increase households’ electricity consumption.
Senza sufficienza la efficienza non basta Morosini 2017morosini1952
Senza sufficienza, l’efficienza non basta. L’ effetto rebound
Marco Morosini, Politecnico federale di Zurigo, marcomorosini.eu mamo@ethz.ch
19.5.2017
------------------------------------
Effetto rebound
(paradosso dell’efficienza, paradosso di Jevons)
L’aumento dell’efficienza energetica
abbassa il prezzo dei servizi energetici
e ne aumenta la accessibilità e il consumo complessivo.
Da millenni l’aumento dell’efficienza energetica
è il principale presupposto per
l’aumento del consumo totale di energia.
----------------------
Riassunto
1. Tutte le conversioni antropiche dell’energia
hanno costi sociali e ambientali.
Nessuna tecnologia energetica può espandersi all’infinito.
2. L’efficienza è ambivalente:
- può far diminuire il consumo di energia in settori saturabili
(es. riscaldamento, climatizzazione)
- ma permette di far crescere l’uso totale di energia
(effetto rebound)
nella società nel suo complesso e in settori non saturabili
(es. trasporti privati, beni di consumo, viaggi, vacanze)
3. Un limite volontario a 2000 watt pro capite sviluppa una parsimonia energetica, che include anche la parsimonia di energie fossili
4. Una “società a 2000 watt” nei Paesi industriali
offre ai Paesi in via di sviluppo un esempio
di organizzazione sociale e di tecnologie sostenibili.
5. La sufficienza necessaria è:
- individuale: moderazione volontaria dei consumi
- collettiva: incentivi, disincentivi, prescrizioni, divieti
Conclusione
Sufficienza e efficienza devono essere sempre insieme nell’agenda culturale e in quella politica
http://www.greengrowthknowledge.org/resource/green-growth-unravelled-how-rebound-effects-baffle-sustainability-targets-when-economy
http://www.greengrowthknowledge.org/sites/default/files/downloads/resource/GG_Unravelled_HBF_and_WI.pdf
Cointegration relationship betweeCOINTEGRATION RELATIONSHIP BETWEEN ECONOMIC ...aeijjournal
Energy dependent small developing island states are besieged to sustain potential rate of growth. This is
due to increase in energy prices and lack of evidence based policy on long term sustainable energy use.
This paper examines the long run relationship between economic growth, export and electricity
consumption in Fiji over the period 1981-2011. Employing Granger causality test it is found that there is
cointegrating relationship between economic growth, export and electricity consumption. The casual
relationship between the variables was investigated within the error correction model framework. We
found that in the long run causality runs from electricity consumption and export to economic growth.
Based on this empirical analysis some important policy implications are suggested.
The changing environmental scenario and depleting conventional energy resources has impelled the world to seek out alternative sources of energy. Mankind is consciously moving towards the use of renewable energy, with the prime focus on alleviating dependency on conventional energy sources. Acting in this direction, the United Nations (UN) has started a global initiative - ‘Sustainable Energy for All’ - to drive the use of renewable energy.
The Global Survey of the Electrical Energy Distribution System: A ReviewIJECEIAES
This paper gives a review of energy scenario in India and other countries. Today’s demand of the world is to minimize greenhouse gas emissions, during the production of electricity. Henceforth over the world, the production of electrical power is changing by introducing abundantly available renewable energy sources like sun and wind. But, because of the intermittent nature of sustainable power sources, the electrical power network faces many problems, during the transmission and distribution of electricity. For resolving these issues, Electrical Energy Storage (EES) is acknowledged as supporting technology. This paper discusses about the world electrical energy scenario with top renowned developed countries in power generation and consumption. Contribution of traditional power sources changed after the introduction of renewable energy sources like sun and wind. Worldwide Agencies are formed like International Energy Agency (IEA), The Central Intelligence Agency, (CIS) etc. The main aim of these agencies is to provide reliable, affordable and clean energy. This paper will discuss about the regulatory authority and government policies/incentives taken by different countries. At the end of this paper, author focuses on obstacles in implementation, development and benefits of renewable energy.
This is the booklet that accompanies BP's Energy Outlook 2030 presentation.
We hope that sharing this outlook contributes to the wider debate on global energy issues. It identifies long-term energy trends, building on our Statistical Review of World Energy, and then develops projections for world energy markets to 2030, taking account of the potential evolution of the world economy, policy, and technology.
The outlook reflects a ‘to the best of our knowledge’ assessment of the world’s likely path from today’s vantage point, drawing on expertise both within and outside the company. It is not a statement about how we would like the market to evolve.
The outlook highlights the growing role of developing economies in global energy consumption, and the increasing share of non-fossil fuels in global energy supply. It emphasizes the importance of both improving energy efficiency and expanding energy supplies to meet the energy needs of billions of people who aspire to better lifestyles, and doing so in a way that is sustainable and secure. This year’s edition has a special focus on the role of shale gas and tight oil in supporting the growth of gas and oil supply. It also notes the uncertainties attached to any long term projection. The discipline of building a numerical projection sharpens our thinking, but the precise numbers are less important than the underlying story of the challenges we all face and the choices we make in producing and consuming energy.
For more information and to download summary tables in Excel format, please visit: http://bit.ly/BPEO2013
This presentation discusses a generalized approach to handle industrial energy use data as compared to production data, in order to predict possible savings
Presentation on Energy consumption and measures to save power in facilities operated by ACUAMED, by Gabriela Mañueco, Acuamed at International at 2014 UN-Water Annual International Zaragoza Conference. Preparing for World Water Day 2014: Partnerships for improving water and energy access, efficiency and sustainability. 13-16 January 2014.
This paper presents findings from a qualitative study of people's everyday interactions with energy-consuming products and systems in the home. Initial results from a large online survey are also considered. This research focuses not only on "conservation behavior" but importantly investigates interactions with technology that may be characterized as "normal consumption" or "over-consumption." A novel vocabulary for analyzing and designing energy-conserving interactions is proposed based on our findings, including: cutting, trimming, switching, upgrading, and shifting. Using the proposed vocabulary, and informed by theoretical developments from various literatures, this paper demonstrates ways in which everyday interactions with technology in the home are performed without conscious consideration of energy consumption but rather are unconscious, habitual, and irrational. Implications for the design of energy-conserving interactions with technology and broader challenges for HCI research are proposed.
ECMFA 2015 - Energy Consumption Analysis and Design with Foundational UMLLuca Berardinelli
Wireless Sensor Networks (WSN) are nowadays applied to a
wide set of domains (e.g., security, health). WSN are networks of spatially distributed, radio-communicating, battery-powered, autonomous sensor nodes. WSN are characterized by scarcity of resources, hence an application running on them should carefully manage its resources. The most critical resource in WSN is the nodes’ battery.
In this paper, we propose model-based engineering facilities to analyze the energy consumption and to develop energy-aware applications for WSN that are based on Agilla Middleware. For this aim i) we extend the Agilla Instruction Set with the new battery instruction able to retrieve the battery Voltage of a WSN node at run-time; ii) we measure the energy that the execution of each Agilla instruction consumes on a target platform; and iii) we extend the Agilla Modeling Framework with a new analysis that, leveraging the conducted energy consumption measurements, predicts the energy required by the Agilla agents running on the WSN. Such analysis, implemented in fUML, is based on simulation and it guides the design of WSN applications that guarantee low energy consumption. The approach is showed on the Reader agent used in the Wild Fire Tracker Application.
Free Cooling: A Complete Solution on Reducing Total Energy Consumption for Te...Ehsan B. Haghighi
Accordingly the number of telecommunication base stations is increasing all over the world. Consequently network operators are looking for smart energy management architecture for their base stations. This presentation addresses more energy efficient cooling strategies in order to reduce this figure. Combining air conditioning and free cooling systems (e.g. extracting fresh air into the envelope for the latter) is one of the promising and well-proven methods to reduce energy consumption in base stations. Furthermore, the potential of employing free cooling in either single zone (IT/electronic equipment and batteries in one envelope), or dual zones (IT/electronic equipment and batteries in two separate envelopes) strategies are investigated.
Promoting Massive Renewable Energy (RE) Projects
towards achieving Sustainable Development in Nigeria
Taiwo Benjamin
Carleton University, Canada
Presented at #naee2015
ELECTRICITY CONSUMPTION AND ECONOMIC GROWTH IN SWAZILANDpaperpublications3
Abstract: The issue of causality between electricity consumption and economic growth (GDP) has been a topic concerning energy economists’ for a number of years given that the results have important implications for policy makers. This interest has been stimulated by the persistent increase in the awareness of global warming and climate change. Furthermore, this issue is currently of fundamental importance given the very real threat of global warming and hence the need to cut electricity consumption to reduce emissions to help stem climate change. Renewable energy plays a vital role in economic growth. Energy consumption is, in Africa, one of the mostly consumed capital goods for economic growth realization, and it has nowadays become a need for the society to function properly.
CAUSALITY EFFECT OF ENERGY CONSUMPTION AND ECONOMIC GROWTH IN NIGERIA (1980-2...paperpublications3
Abstract: This paper investigates the causality effect of energy consumption and economic growth in Nigeria using annual data from the World Bank Development Indicator and CBN Statistical Bulletin from1980 to 2012.The paper adopts Vector Auto Regressive (VAR) and Error Correction Model (ECM) to test the causality between energy consumption and economic growth in Nigeria. The order of integration of the variables was determined using Augmented Dickey Fuller (ADF) test and the DF-GLS test which was followed by co-integration and causality test. Our findings suggest a positive relationship between energy consumption and economic growth. There is no causality between energy consumption and economic growth in the short run; in the long run we find unidirectional causality running from Economic growth to Energy consumption. There is need for government to diversify the energy mix to include all the untapped potentials of renewable power options such as small hydro, wind, solar and biomass among others in all the states and local constituencies. Energy conservation policy is necessary to adopt if this causality is running from per capita GDP to energy consumption but policy should be designed in a way that energy conservation measures do not adversely affect the economic growth.
Keywords: Causality, Economic Growth, Energy consumption, Energy Conservation Policy, Error correction Model, Per Capita GDP.
Global Power Grid Interconnection for Sustainable Growth: Concept, Project an...Power System Operation
Environment friendly and low carbon RE has a great development and research potential for GEI. Acceleration in development of clean energy is required in future to improve the proportion in world's energy generation. Low-cost conversion and plug in play type high-efficiency generation are required to develop in energy bases, especially on North Pole and Equator regions. Clean energy topic is generally divided into generation bases, grid integration and large-scale energy storage system. Hydropower is the key source of clean energy against power grid fluctuations due to intermittent sources. It has capabilities such as fast response on dispatch command, easy to start/shutdown, large capacity and high efficiency as well as flexible on load adjustment. However, large hydro sources can be improved further for eco-friendly point of
view.
CAUSALITY EFFECT OF ENERGY CONSUMPTION AND ECONOMIC GROWTH IN NIGERIA (1980-2...paperpublications3
Abstract: This paper investigates the causality effect of energy consumption and economic growth in Nigeria using annual data from the World Bank Development Indicator and CBN Statistical Bulletin from1980 to 2012.The paper adopts Vector Auto Regressive (VAR) and Error Correction Model (ECM) to test the causality between energy consumption and economic growth in Nigeria. The order of integration of the variables was determined using Augmented Dickey Fuller (ADF) test and the DF-GLS test which was followed by co-integration and causality test. Our findings suggest a positive relationship between energy consumption and economic growth. There is no causality between energy consumption and economic growth in the short run; in the long run we find unidirectional causality running from Economic growth to Energy consumption. There is need for government to diversify the energy mix to include all the untapped potentials of renewable power options such as small hydro, wind, solar and biomass among others in all the states and local constituencies. Energy conservation policy is necessary to adopt if this causality is running from per capita GDP to energy consumption but policy should be designed in a way that energy conservation measures do not adversely affect the economic growth.
Before we kick-off a new line-up of insightful studies and conversations on energy this 2021, we take a snapshot of the previous working papers which were featured last year.
These studies were produced under the Access to Sustainable Energy Programme-Clean Energy Living Laboratories (ASEP-CELLs) project implemented by the Ateneo School of Government (ASOG), and funded by the European Union.
To receive updates on our latest events and publications, please subscribe to our mailing list through this link: http://bit.ly/ASEPCELLsMailingList
The Economics of Transitioning to Renewable Energy SourcesChristo Ananth
Christo Ananth, Rajini K R Karduri, "The Economics of Transitioning to Renewable Energy Sources", International Journal of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), Volume 6,Issue 2,February 2020,pp:61-68
Investigating the residential electricity consumption-income nexus in Morocc...IJECEIAES
In a comprehensive LMDI-STIRPAT-ARDL framework, this research investigates the residential electricity consumption (REC)-income nexus in Morocco for the period 1990 to 2018. The logarithmic mean Divisia index (LMDI) results show that economic activity and electricity intensity are the leading drivers of Morocco’s REC, followed by population and residential structure. And then, the LMDI analysis was combined with stochastic impacts by regression on population, affluence, and technology (STIRPAT) analysis and the bounds testing approach to search for a long-run equilibrium relationship. The empirical results show that REC, economic growth, urbanization, and electricity intensity are cointegrated. The results further show that there exists a U-shaped relationship between per capita gross domestic product (GDP) and REC: an increase in per capita GDP reduces REC initially; but, after reaching a turning point (the GDPPC level of 17,145.22 Dh), further increases in per capita GDP increase REC. Regarding urbanization, the results reveal that it has no significant impact on Morocco’s REC. The stability parameters of the short and long-term coefficients of residential electricity demand function are tested. The results of these tests showed a stable pattern. Finally, based on the findings mentioned above, policy implications for guiding the country's development and electricity planning under energy and environmental constraints are given.
Global issue based power generation expansion planning for a power systemeSAT Journals
Abstract In This Project an global issue based power generation expansion planning model has been developed for optimization that considers the growth of fuel prices and its fluctuation, benefits of carbon-trading in generation expansion decision, power risks . The Developed model is applied to An Electric Power System for the future. In an electric power system, the electric demand has been running ahead of supply. In addition, of the growth of fuel price can affect the country economy. To minimize these problems, a multi-objective model preceded by electric demand forecasting is developed by considering the unit of power generation and investment cost, imported fuels and benefits of carbon trading, environmental impacts. A model has been developed in the MATLAB and is simulated to get the output of each power plant at a very nominal cost for building the power plants, production and fuel. Keywords: electrical power generation, expansion, planning, Load forecasting, Optimizing, linear model
Dynamic Linkages between Electricity Consumption, Urbanization and Economic G...AkashSharma618775
During the last decades, the relationship between electricity consumption, urbanization and economic
growth has been well documented in the energy economics literature. In term of our present case, limited research
had been conducted for GCC countries. This study is an addition to the existing literature by empirically
investigates the relationship between economic growth, electricity consumption, and urbanization in the Gulf. A
standard growth models will be estimated using both fixed-effects and random effects models. In addition, panel
unit root and panel co-integration tests will be employed to check for the efficiency of the data. The long run
relationship is estimated using fully modified OLS and: Panel Dynamic Least Squares (DOLS) methods. Panel
Vector Error Correction Model (VECM) is also utilized in this study.
The study found that there exists a long relationship between GDP per capita electricity consumption, Urban
population, inflation, and degree of openness. The degree of adjustment was found to be 0.43 percent, meaning
that any deviation for FDI from its long run path will be corrected by 0.43 percent each year.
The main policy implication for GCC to have reasonable level of growth depends on their ability to develop and
utilize the effective use of electricity power. The study suggests that to move away from oil which is fluctuate over
time to establishing a good base for industrialization by the shift of utilizing a strict balance between electricity
consumption and urbanization rate which it doesn’t affect in the long run the climate change.
A Comparative Analysis of Renewable Energy Policies and its Impact on Economi...ssuser793b4e
Renewable energy has been identified as a critical component of
global efforts to address climate change, enhance energy security, and foster
sustainable economic growth. As a result, many countries have implemented
renewable energy policies to promote the development and deployment of
renewable energy technologies. However, the impact of these policies on
economic growth remains a subject of debate. This article provides a
comparative analysis of renewable energy policies and their impact on
economic growth. The study employs a systematic review of the literature and
utilizes qualitative and quantitative methods to compare renewable energy
policies and their economic impacts across different countries. The findings
suggest that the impact of renewable energy policies on economic growth
varies across countries and is influenced by factors such as policy design,
institutional context, and economic structure. This research article finally,
examined the challenges associated with implementing renewable energy
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Modeling and forecasting energy consumption in ghana
1. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
Modeling and Forecasting Energy Consumption in Ghana
Godfred Kwame Abledu(PhD)
School of Applied Science and Technology, Koforidua Polytechnic, PO Box 981, Koforidua, Ghana
E-mail of the corresponding author: godfredabledu@gmail.com
Abstract
Energy is a key infrastructural element for economic growth. It is a multitalented item that underpins a
wide range of products and services that improve the quality of life, increase worker productivity and encourage
entrepreneurial activity. This makes Energy consumption to be positively and highly correlated with real per
capita GDP. In Ghana, between 2000 and 2008, while real per capita GDP growth averaged 5.5% per annum,
annual Energy consumption growth averaged 1.21%. Inspite of the fact that real per capita GDP and Energy
consumption are positively correlated, it is still not clear the direction of causality between real per capita GDP
and Energy consumption.
These underscore the importance of and the need to develop modeling and forecasting tools as
strategies for long-term planning. Herein lays the motivation for studying and modeling patterns of energy
consumption the Ghanaian economy using seasonal ARIMA models. We obtained historical data of average
monthly maximum energy consumption for the period 2001-2011 for the studies and those of 2009 for forecast
validation of the chosen model, from the Ministry of energy. Model identification was by visual inspection of
both the sample ACF and sample PACF to postulate many possible models and then use the model selection
criterion of Residual Sum of Square RSS , Akaike’s Information Criterion AIC complemented with the
Schwartz’s Bayesian Criterion SBC, to choose the best model. The chosen model is the SARIMA (1, 1, 1) (0, 1,
2) process which met the criterion of model parsimony with low AIC value of -845.79253 and SBC value of 812.34153. Model adequacy checks shows that the model is appropriate. The model was used to forecast energy
consumption for 2013 and the forecast compared very well with the observed empirical data for 2012.
1.0. Introduction
Energy is a crucial input for production, manufacturing, and commercial activities. The importance of
energy has increased in all fields as it is one of the fundamental inputs for economic development. On the other
hand, energy consumption is permanently rising as the world population increases and technology advances.
Therefore due to its leading role it in economic development and technological progress, the focus of the study is
energy consumption. In 2008, the energy consumption of industrial sector accounted for 46% of total energy
consumption, while services, agriculture, forestry and fishing sectors; households and transportation sector
consumed 29%, 24% and less than 1% respectively. From 2000 to 2008 the energy consumption increased by
55%. Growth was slower for industrial sector (55%) and households (53%), but much faster at service sector
(75%). Annual per citizen consumption is around 2.3 MWh that is one-quarter of International Energy Agency
(IEA) average (IEA, 2009).
Energy consumption in Ghana is estimated to be increasing by 10% per annum due to the demand from
the growing population. However, current sources of production (hydro and thermal facilities) generate only
66% of the current demand. Considering current trends, it is difficult to substantiate these basic facts, because of
the lack of information. As a result, research into the existing sources of generating energy, energy consumption
and prospective projects has been performed. This was achieved using three key techniques; review of literature,
empirical studies and modelling. The results presented suggest that, current annual installed capacity of energy
generation (i.e. 1960 MW) must be increased to 9,405.59 MW, assuming 85% plant availability. This is then
capable to coop with the growing demand and it would give access to the entire population as well as support
commercial and industrial activities for the growth of the economy. The prospect of performing this research is
with the expectation to present an academic research agenda for further exploration into the subject area, without
which the growth of the country would be stagnant.
Energy consumption in Ghana is estimated to be increasing by 10% per annum due to the demand from
the growing population. However, current baseline production sources generate only 66% of the current demand.
From this, an estimated 65% is used in the industrial and service sectors while the residential sector accounts for
about 47% of total energy consumed in the country. Though this does not add up (certainly there must be
justified reason), this is what has been presented in the Energy Sector Strategy and Development Plan, 2010
(www.ghanaoilwatch.org). This lack of parity prompts research to enable the validation of available data.
Reliable forecasting is an important part of planning and demand management of energy utilities
(Harris & Lui, 1993). Residential Energy demand depends on a number of variables including weather, seasons,
and technological factors to name a few (Stern, 1984). Utility forecasters are tasked with developing models that
1
2. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
accurately predict demand. An extensive summary of energy modeling can be found in Hartman (1979) and
Bunn and Farmer (1985).
Although there are a number of model types that can be used to investigate residential Energy demand,
this study employs SARIMA model using basic input variables. Numerous researchers have published work
ranging from selecting the appropriate method of forecasting (Chase, 1997) to how to use specific forecasting
approaches in spreadsheets (Albright, Winston, & Zappe, 2005; Grossman, 1999; Kros, 2009; Ragsdale, 2006;
Savage, 2003). Others have shown that the most commonly used method of forecasting is based on linear
multiple regression (Brockwell and Davis, 1991).
Radovilsky and Eyck (2000) provided a much needed discussion on forecasting with Excel. Utility
companies typically need forecasts that cover different time spans to achieve operational, tactical, and strategic
intents. Utility firms know that seasonal variations impact demand, namely high and low energy consumptions
and the four basic seasons. Technological advances and time also plays a role in demand as well as price.
Technological advances combined with ‘‘green’’ initiatives on a consumer level such as solar water heaters,
solar photovoltaic panels, and wind turbines directly impact residential demand.
Time plays a role in demand as residential housing square footage has increased and residential
customers tend to use more Energy over time. Although price does impact residential energy demand, for the
time frame of this study price has held approximately steady and will not be included in the model.
Utility companies typically need forecasts that cover different time spans to achieve operational, tactical,
and strategic intents. Utility firms know that seasonal variations impact demand, namely high and low energy
consumptions and the four basic seasons. Technological advances and time also plays a role in demand as well
as price. Technological advances combined with ‘‘green’’ initiatives on a consumer level such as solar water
heaters, solar photovoltaic panels, and wind turbines directly impact residential demand.
Time plays a role in demand as residential housing square footage has increased and residential
customers tend to use more Energy over time. Although price does impact residential energyal demand, for the
time frame of this study price has held approximately steady and will not be included in the model. Ragsdale
(2006) argue that most of the literature on energy and economic development discusses how development affects
energy use rather than vice versa. This strand of literature considers economic growth as the main driver for
energy demand and only advanced economies with a high degree of innovation capacity can decrease energy
consumption without reducing economic growth.
Bunn and Farmer (1985), on the other hand, have stressed the importance of considering the effect of
changes in energy supply on economic growth in both developed and developing countries. If energy supply is
considered a homogenous input for the production function, this means that if policy constraints affect energy
supply, economic development is harmed. When energy services are differentiated, emphasizing the existence of
higher and lower-quality forms of energy, society should make a choice in terms of an optimal energy mix,
considering that higher quality energy services could produce increasing returns to scale. This means that energy
regulation policies supporting the shift from lower-quality (typically less efficient and more polluting) to higherquality energy services could provide impulse to economic growth rather than be detrimental. If we consider
energy consumption as a function of economic output, regulation and technical innovation, a suitable
representation is the formalization expressed in equation 1.
(
Yij = f K ij. Lij , ECij ( Pij )
)
(1)
where economic output (Y) is a function of the capital stock (K), labour (L) and energy inputs (EC), here
modelled as being strictly dependent on energy prices (p). This simple assumption is required if we consider that
energy supply is often affected by exogenous elements such as international energy prices and public regulation,
assuming that public regulation can be fully expressed by domestic energy prices. We are aware that this is a
simplification but we also know that, in many cases, energy taxes in OECD countries constitute the greatest part
of energy prices.
These alternative views have important policy implications concerning, for example, aspects such as the
development level of the considered country or the distributive effects related to the introduction of stringent
energy (and environmental) regulations.
By observing energy trends in the past five decades, energy used per unit of economic output (energy
intensity) seems to have steadily declined especially in advanced economies. The principal reason for this
evidence is the shift in energy use from direct use of fossil fuels to the use of higher quality fuels (from coal to
natural gas) or Energy.
If we consider highly industrialized countries, total energy use has increased, energy efficiency has
improved and energy intensity - the energy necessary to produce output - has steadily fallen, especially in the
industrial sector. Stabilization of greenhouse gas concentrations requires reductions in fossil fuel energy use
which is a major essential input throughout all modern economies. If energy conservation and a switch from
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3. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
fossil fuels to alternative energy sources can be effected using new energy efficient technologies, the trade-off
between energy and growth becomes less severe.
In order to obtain decoupled trends in the energy and economic sectors, an effort should be explicitly
directed to possible win-win outcomes of energy (and environmental) regulation policies which are oriented
towards technological innovation and productivity improvements. There are also changes in energy intensity that
are not directly related to changes in the relative energy price but mainly explained by structural change in the
productive composition (Stern, 1984). If the development process is in the deindustrialization phase, the
increasing importance of value added produced by the service sector could lead to a global reduction in energy
consumption due to a minor weight represented by energy-intensive industrial sectors.
The main focus of this work is to determine appropriate seasonal ARIMA model that can adequately
predicts energy consumption in Ghana. The seasonal multiplicative ARIMA (Autoregressive, Integrated Moving
Average) model is of the form
φ ( B ) Φ ( B s ) Zt = C + θ ( B ) Θ ( B s ) a t
(2)
D
where
Zt = ∇ d ∇ log yt , yt is the observed energy consumption data at time t, ∇ = 1 − B is the regular
∇ = 1 − B s is the seasonal difference. D is the order of the seasonal difference while d is the
order of regular difference. C is a constant and at is a white noise process. φ ( B ) is the regular autoregressive
difference and
( ) is the seasonal autoregressive polynomial of order P. Similarly, θ ( B ) is
the regular moving average polynomial of order q while Θ ( B ) is the seasonal moving average polynomial of
polynomial of order p while Φ B
s
s
order Q. Sometimes, the model (2) is denoted SARIMA (p, d, q)(P, D, Q). The ARIMA model (2) is said to be
invertible if all the roots of the moving average polynomial
θ ( B ) Θ ( B s ) lie outside the unit circle.
Note that the model is already stationary. Many models can be formed from (2).These models are made
of either past observed values together with a white noise or white noise only or a mixture of both. The major
contribution of Box and Jenkins were to provide a general strategy in which three stages of model building were
given prominence. These stages are those of model identification, estimation and diagnostic checks (Hipel et al.
1977 and McLeod, 1995).
2.0. Materials and Method
To test SARIMA models, historical data of average monthly maximum energy consumption for the
period 2001-2011 was obtained from the Energy Commission in Ghana. Data for the first 72 months was used to
fit the SARIMA models and the last 12 months as a hold-out period to evaluate forecasting performance and to
monitor the energy consumption.
Before fitting a SARIMA model, the time series must be checked for violations of the weak stationarity
assumption of the models (Brockwell and Davis, 2002). In SARIMA models, trend and seasonal
nonstationarities are handled directly by the model Structure so that only the nonstationarity of variance needs to
be addressed before model fitting.
The SARIMA models were fitted to the data using a semi-automated approach based on a combination
of the Box-Jenkins method with small-sample, bias-corrected Akaike information criteria (AIC,) model selection
(Brockwell and Davis, 2002). This approach involved three major steps: 1) selection of the candidate model set;
2) estimation of the model and determination of AIC,; and 3) a diagnostic check.
To detect possible presence of seasonality, trend, time varying variance and other nonlinear phenomena,
the time plot of the observed data was inspected side by side with the plots of sample autocorrelation functions
(ACF) and sample partial autocorrelation functions (PACF). This would help to determine possible order of
differencing and and the necessity of logarithmic transform to stabilize variance. Non stationary behavior was
indicated by the refusal of both the ACF values, ρ n and the PACF, φnn to die out quickly. Also possible
seasonal differencing was indicated by large ACF values,
ρn
at lags s, 2s, …,ns. The technique was to apply
both simple and seasonal differencing until the data was stationary. Stationary behavior was indicated by either a
cut or exponential decay of ACF values ρ n as well as PACF values φnn .
Model identification was by comparing the theoretical patterns of the ACF and PACF of the various
ARIMA models with that of the sample ACF and PACF computed using empirical data. A suitable model was
inferred by matching these patterns. Generally ( Box and Jenkins, 1976), ARIMA (0, d, q) was indicated by
3
4. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
spikes up to lag q and a cut to zero thereafter of the ACF values ρ n complemented by an exponential decay or
damped sine wave of the PACF values φnn .
ρn
ARIMA (p, d, 0 ) was identified by exponential decay or damped sine wave of the of the ACF values
complemented spikes up to lag p and a cut thereafter to zero of the PACF values φnn .When the process was
an ARIMA(0, d, q)*(0, D, Q) then spikes would be noticed up to lag q + Qs. While ARIMA (p, d, 0)*(P, D, 0)
was indicated by spikes at lag p+Ps and a cut to zero thereafter of the PACF.
Selection of the candidate model set was carried out by first analyzing sample estimates of the
autocorrelation function (ACF) and partial autocorrelation function (PACF) in order to select the three major
orders of the SARIMA models: d, D, and S. In this work, the model identification discussed above was used to
give a rough guess of possible values p, q, P, and Q from which several models were postulated and then used
the model selection criterion of Residual Sum of Square RSS (Box and Jenkins, 1976), Akaike’s Information
Criterion AIC (Akaike, 1974) to choose the best model. The AIC computation is based on the mathematical
formula AIC = −2log L + 2m , where m = p + q + P + Q is the number of parameters in the model and L
is the likelihood function. The best model was the one with the lowest AIC value. It was however noted that the
likelihood was likely increased by addition of more parameters into the model. This would further reduce the
value of the AIC leading to the choice of a model with many parameters. Brokwell and Davis (2002) emphasize
on the need for the chosen model to meet criterion of model adequacy and parsimony. For this reason the RSS
and AIC were complemented with the Schwartz’s Bayesian Criterion SBC.
The SBC computation was based on the mathematical formula SBC = −1log L + mLogn , where
m = p + q + P + Q is the number of parameters in the model and L is is the likelihood function. The SBC
introduced a penalty function to check excess parameters in the model having identified a suitable SARIMA
model, the next stage is the parameters estimation of the identified model and this is done through an exact
maximum likelihood estimate due to Brockwell and Davis (2002). While forecast and prediction is by least
squares forecast using a least square algorithm due to Brokwell and Davis (2002). When the estimated
parameters are not significant, we do correlation analysis to remove redundant parameters.
The test for model adequacy stage requires residual analysis and this was done by inspecting the ACF
of the residual obtained by fitting the identified model. If the model was adequate then residuals should be a
white noise process. Under the assumption that the residual was a white noise process, the standard error of the
1
(Anderson, 1942. Hence under the noise
n
±1.96
. If more than 5% fall
assumption, 95% of the autocorrelation functions should fall within the range
n
autocorrelation functions should be approximately equal to
outside this range, then the residual process was not white noise . the visual inspection of the residual ACF was
complemented with the portmanteau test of Ljung and Box, 1978. This test provided a Q statistics defined by
m
Q = n(n + 2)∑ (n − k ) −1 rk2
(3)
k =1
where
rk is the autocorrelation value of the residual at lag k, n = N − d − D . Q is approximately distributed as
χ 2 ( m − p − q − P − Q ) . The technique here was to choose a level of significance and compare the computed
χ2
m − p − q − P − Q degree of freedom. If the model was inappropriate, the Q
2
value would be inflated when compared with the tabulated χ .
Q with the tabulated
with
Model estimation was carried out by using maximum likelihood methods, after conditional sum of
squares estimation of the starting values (Brockwell and Davis, 2002). Diagnostic checks on the AIC-selected
model involved the following steps: 1) verification of the resemblance of residuals to white noise (ACF plots,
Ljung-Box test, cumulative periodogram test); 2) tests on the normality of residuals (Jarque-Bera and ShapiroWilks tests); and 3) confirmation of model stationarity, invertibility, and parameter redundancy (Ljung and Box,
1978). All tests were carried out at a significance level of a=0.05. The variance explained by the model was
2
ˆ2
determined as 1 − σ / σ yt .
4
5. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
3.0.Forecasts and model performance
The 12 months of model forecasts was evaluated, using the last month of the fitting data set as the
ˆ
forecast origin (2013). Forecasts were obtained in the mean- centered transformed scale ( yh , h = 1,...,12 ) and
ˆ
in the original scale of the data ( xh , h = 1,...,12 ), after correcting for back-transformation bias.
ˆ
SARIMA model performance was assessed by comparing h-step forecasts ( xh and
energy consumption observed between 2011 and 2012(
forecast errors ( eh
ˆ
yh ) with monthly
xh and yh ). This was done by evaluating monthly
ˆ
= xh − xh ) and then considering a set of accuracy measures: 1) annual root mean-square
error (RMSE); 2) mean error (ME); 3) absolute percent error (APEh); 4) mean absolute percent error (MAPE);
and 5) annual Percent error (PE) . From these, RMSE was evaluated in the transformed scale to allow its
comparison to 6, and all others were computed in the more user-friendly original scale of the data. Additionally,
we compared the forecasting performance of the SARIMA model against two simple naive forecasting models
(naïve model 1 or NM1, and naive model 2 or NM2.
To monitor the energy consumption we used two types of PIs(Anderson, 1942): single step PIs (PIss,h)
and multistep PIs (PIms,h). Single step PIs refer to a single monthly forecast (e.g., h=3) and are useful for
determining whether a specific monthly observation is an outlier at a given significance level α . Multistep PIs
encompass a 1 − α prediction region that is a simultaneous PI for all observations registered up to a certain hstep and are useful in detecting systematic departures from historical patterns. We calculated PIss,h as
ˆ
yh = ±tdf ,α/2 PMSEh where PMSEh is the expected mean squared prediction error at step h and df=N-DS-d-r.
In the calculation of multistep PIs, we used a conservative approach based on a first-order Bonferroni inequality,
whereby Plms,h is given as
ˆ
yh = ±tdf ,α/2h PMSEh and joint prediction intervals of, at least, 1 − α around the
point forecasts are obtained.
4.0.Empirical Results
To decide on the presence of trend and time varying variances, the time plot of maximum energy
consumption data in Figure 1 was inspected side by side with the ACF and PACF of the data as shown in Figure
2.
There is a systematic change in the time plot in Figure 1 which is not periodic. This indicates that the
pattern of Ghana’s total energy consumption is either decreasing or not. Total production was low after 1991 and
we could attribute this to the low rainfall and increase in population and industries.. There was a sharp rise in
production from 1997 to 1999, after which it drastically declined. In general, the trend of Ghana’s total energy
consumption follows an upward and downward movement. The figure exhibits a moving trend, hence there is
the need to apply the method of differencing to attain stationarity since the trend describing the data shows non
stationarity. The time series analysis of the energy consumption data was conducted using the R programme.
First, the behavior of the ACF for the time series was examined. The autocorrelation function of
Ghana’s total energy consumption is shown in Figure 2. The plot of the ACF function against the lag is called
the correlogram. A trend in the data shows in the correlogram as a slow decay in the autocorrelation which
depicts a downward slopping due to the exponential nature of the plot. It describes the correlation between
values of Ghana’s total energy consumption at different points in time, as a function of the time difference. The
autocorrelation function is decreasing and that shows there is a trend in Ghana’s total energy consumption data.
To remove the trend component from the data, the data was differenced. Figure 3 is a transformation of
Ghana’s total energy consumption using first differencing method. The observation does not revert to its mean
value. The transformation of the data with the first differencing displays characteristics that suggest non
stationary. Due to this it is necessary to make another transformation so as to produce a new series that is more
compatible with the assumption of stationarity. In general, the first difference plot in Figure 4 reveal a little bit of
variability. Hence the second differencing is employed.
Differencing the data the second time shows some variability and hence the data is still not stationary.
Therefore a third differencing was applied to the data. A transformation was performed on Ghana’s total energy
consumption data using the third differencing method to remove the trend component in the original data, as
shown in Figure 5. The observations move irregularly but revert to its mean value and the variability is also
approximately constant. The data of Ghana’s total energy consumption seemed to be approximately stable.
Hence a seasonal ARIMA is expected of the process of the form SARIMA ( p,1, q )( P ,1, Q )12 . The KPSS
unit root test for stationarity of the differenced energy consumption data is shown in Table 2. From Table 2, the
p-value of 0.1 is greater than 0.05, so the stationarity holds for the final series.
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6. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
The order of the model parameters p, q, P and Q were identified by visual inspection of ACF and
PACF of the stationary process of the maximum energy consumption shown in Figure 6 to propose many
possible models and the use of model selection criterion of AIC and BIC to pick the most appropriate model.
The expectation was that the ACF in Figure 6 would cut at q+Qs. However a cut after lag 25 is noticed
suggesting a moving average parameter of order one i.e. q=1 and a seasonal moving average parameter of order
two i.e. Q=2. The plot exhibits an alternating sequence of positive and negative spikes. Such a pattern in the
autocorrelation plot is signature of a sinusoidal model.
Similarly from the PACF in Figure 6, we notice a cut at lag 25 suggesting an AR parameter of order one
i.e.p=1 and a Seasonal autoregressive parameter of order two i.e. P=2. Since the strategy was not to have mixed
seasonal factors, two models were postulated from which, based on the model selection criterion of RSES, AIC
and SBC, the best was selected.
The two models are SARIMA (1, 1, 1) (0, 1, 2) and SARIMA (1, 1, 1) (2, 1, 0). The search was
extended to models around the two already mentioned. The result is shown in table 1. From table 1, we note that
in terms of AIC and SBC, the SARIMA (1, 1, 1) (0, 1, 2) model performed best. However it is in competition
with SARIMA (1, 1, 1) (1, 1, 2) that has the lowest RSES. This notwithstanding, SARIMA (1, 1, 1) (0, 1, 2) was
chosen as the best in terms of model parsimony and performance based on AIC and BIC. The parameter values
of the chosen model were estimated as shown(Table 2).
5.0.Parameter Estimation:
The model parameters were estimated by the method of Maximum Likelihood Estimates for each of the
ARIMA model. From Table 3, the estimated coefficients of ARIMA (1, 1, 0) and ARIMA (0, 1, 1) are
statistically significant. The estimated coefficients for SARIMA (1, 1, 1) is not statistically significant. SARIMA
(1, 1, 0) is the best model with the minimum Akaike’s Information Criterion (AIC) and Bayesian Information
Criterion (BIC) statistics. The AIC, AICc and BIC are good for all the models but they favour SARIMA (1, 1, 0)
model. The model is selected for forecasting.
We note that all the parameters are significant. The chosen model is mathematically of the form
(1 − 0.2378B )(1 − B ) (1 − B12 ) log yt = (1 − 9172 B ) (1 − 6574 B12 − 0.238912 ) at
(1 − 0.2378B ) xt = (1 − 0.9172 B − 0.6574 B12 − 0.2389 B12 + 0.2173B 25 ) at
xt = 0.2378 xt −1 + at − 0.9172at −1 − 0.2389at − 25 + 0.2173t − 26
where
(
)
xt = (1 − B ) 1 − B12 log yt
To verify the suitability of the model, we plot the autocorrelation values of the residual against lag as
shown in Figure 6. Inspection of Figure 6 reveals there is no spike at any lag indicating that the residual process
is random. We complement with the portmanteau of Ljung and box. Computation of the Q value of the
portmanteau test, using the first 25 autocorrelation values of the residual gives 18.468. When compared with
tabulated chi square value of 32.7, with 21 degree of freedom and at 5% level of significance, we conclude that
the model is a good fit. Table 3 shows the 2013 forecast using SARIMA (1, 1, 1) (0, 1, 2) and empirically
observed data for the year 2012. A t-distribution test of equality of mean shows that the difference between the
two means is not significant at 1% level of significance. We therefore conclude that the chosen model can
adequately be used to forecast maximum energy consumption.
6.0.Conclusion
We have shown that time series ARIMA models can be used to model and forecast Maximum energy
consumption. The identified SARIMA (1, 1, 1) (0, 1, 2) has proved to be adequate in forecasting maximum
energy consumption for at least one year. Researchers will find this result useful in building energy consumption
component into a general economic forecasting model. Also environmental manager who require long term
energy consumption forecast will find the identified model very useful.
The univariate SARIMA model presented a good fit to the short time series of energy consumption,
explaining most of its variance and adequately modeling the seasonality and correlation structure of the data.
Taken together, these results indicate that SARIMA models should be adequate for data sets of monthly energy
consumption in general, and not just those with larger sample sizes. The SARIMA model showed that energy
consumption will continue to increase. The forecasted consumption levels will be a basis for government and
Energy Commission to implement policies and programmes aimed at sustainable production of energy.
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7. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
References
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USA
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Ljung, G. M and Box, G. E. P (1978): On the Measure of Lack of Fit in Time Series Model. Biometrika, Vol. 65,
pp. 297-303.
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Table 1: KPSS Unit Root Test for Stationarity
KPSS Level
0.95
Truncation lag parameter
4
P-value
0.001
Table 2: Postulated Models and Performance Evaluation
Model
RSES
AIC
SBC
SARIMA(1,1,1 )(2, 1, 0 )
0.06814735
-782.4732
-752.4139
SARIMA(1,1,1 )(0, 1, 2 )
0.06574412
-845.3568
-802.0986
SARIMA(1,1,0 )(1, 1, 2 )
0.07084709
-609.7683
-772.1632
SARIMA(1,1,0 )(1, 1, 2 )
0.06978742
-839.7846
-728.3901
SARIMA(0,1,1 )(0, 1, 2 )
0.06524412
-798.3547
-738.3964
SARIMA(0,1,1 )(1, 1, 2 )
0.06579358
-734.0823
-752.4139
SARIMA(0,1,1 )(0, 1, 2 )
0.06326487
-642.3245
-761.9546
SARIMA(0,1,0 )(1, 1, 2 )
0.17576643
-756.2089
-657.8121
SARIMA(0,1,0 )(0, 1, 2 )
0.16556782
-772.6743
-674.0132
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8. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
Table 3: Parameters B in the Model
B
SEB
T-ratio
Approximate Probability
AR1
0.2378
0.06524
4.14711
0.0079434
MA1
0.9172
0.02716
28.05884
0.0000001
SMA1
0.6574
0.15794
5.347132
0.0000794
SMA2
0.2389
0.06872
3.097458
0.0547235
Table 4: Forecast for 2013
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Forecast
33.1
33.5
34.0
32.6
31.8
29.7
28.6
30.4
31.3
32.1
33.7
34.6
Observed
33.4
33.9
34.9
32.7
31.5
29.3
28.9
29.7
32.1
31.0
34.9
33.1
Difference
-0.3
-0.4
-0.9
-0.1
0.3
0.4
-0.3
0.7
-0.8
1.1
-1.2
1.5
8
9. Journal of Energy Technologies and Policy
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.3, No.12, 2013
www.iiste.org
Figure 6: ACF and PACF Plots of Residuals
9
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