This chapter deals with Load forecasting of different power system parts which includes the generation, transmission and distribution systems. This slide is specifically prepared for ASTU 5th year power and control engineering students.
Load forecasting is a process to estimate the need or demand for power from a system. It helps to maximize efficiency and minimize the operational cost of any power generation unit. Some common models are used for this purpose among which the five most used models for load predictions are discussed here.
transmission versus distribution planning, long term versus short term planning,issues in transmission planning,generation planning,capacity resource planning, transmission planning,national and regional planning, integrated resource planning
Electrical fault is the deviation of voltages and currents from nominal values or states. Under normal operating conditions, power system equipment or lines carry normal voltages and currents which results in a safer operation of the system.
This slide is an introductory part of the course Computer Application in Power system. it will describe the basic tasks of a computer and different computer application areas.
Electrical Technology was founded on the remarkable discovery by Faraday that a changing magnetic flux creates an electric field. Out of that discovery, grew the largest and most complex engineering achievement of man : the electric power system. Indeed, life without electricity is now unimaginable. Electric power systems form the basic infrastructure of a country. Even as we read this, electrical energy is being produced at rates in excess of hundreds of giga-watts (1 GW = 1,000,000,000 W). Giant rotors spinning at speeds up to 3000 rotations per minute bring us the energy stored in the potential energy of water, or in fossil fuels. Yet we notice electricity only when the lights go out!
While the basic features of the electrical power system have remained practically unchanged in the past century, but there are some significant milestones in the evolution of electrical power systems.
Load types, estimation, grwoth, forecasting and duration curvesAzfar Rasool
It includes the detail analysis of the various types electrical load, how to estimatate the load, methods of load forecasting and explanation of the load duration curves.
Summary of Modern power system planning part one
"The Forecasting of Growth of Demand for Electrical Energy"
the main topic of this chapter is the analysis of the various techniques required for utility planning engineers to optimally plan the expansion of the electrical power system.
These slides are all about Phasor Measurement Units (PMUs). An introduction to PMU is presented as a preliminary knowledge for the course 'Distribution Generation and Smart Grid'. Your valuable suggestions are welcome.
This chapter deals with Load forecasting of different power system parts which includes the generation, transmission and distribution systems. This slide is specifically prepared for ASTU 5th year power and control engineering students.
Load forecasting is a process to estimate the need or demand for power from a system. It helps to maximize efficiency and minimize the operational cost of any power generation unit. Some common models are used for this purpose among which the five most used models for load predictions are discussed here.
transmission versus distribution planning, long term versus short term planning,issues in transmission planning,generation planning,capacity resource planning, transmission planning,national and regional planning, integrated resource planning
Electrical fault is the deviation of voltages and currents from nominal values or states. Under normal operating conditions, power system equipment or lines carry normal voltages and currents which results in a safer operation of the system.
This slide is an introductory part of the course Computer Application in Power system. it will describe the basic tasks of a computer and different computer application areas.
Electrical Technology was founded on the remarkable discovery by Faraday that a changing magnetic flux creates an electric field. Out of that discovery, grew the largest and most complex engineering achievement of man : the electric power system. Indeed, life without electricity is now unimaginable. Electric power systems form the basic infrastructure of a country. Even as we read this, electrical energy is being produced at rates in excess of hundreds of giga-watts (1 GW = 1,000,000,000 W). Giant rotors spinning at speeds up to 3000 rotations per minute bring us the energy stored in the potential energy of water, or in fossil fuels. Yet we notice electricity only when the lights go out!
While the basic features of the electrical power system have remained practically unchanged in the past century, but there are some significant milestones in the evolution of electrical power systems.
Load types, estimation, grwoth, forecasting and duration curvesAzfar Rasool
It includes the detail analysis of the various types electrical load, how to estimatate the load, methods of load forecasting and explanation of the load duration curves.
Summary of Modern power system planning part one
"The Forecasting of Growth of Demand for Electrical Energy"
the main topic of this chapter is the analysis of the various techniques required for utility planning engineers to optimally plan the expansion of the electrical power system.
These slides are all about Phasor Measurement Units (PMUs). An introduction to PMU is presented as a preliminary knowledge for the course 'Distribution Generation and Smart Grid'. Your valuable suggestions are welcome.
Short term load forecasting system based on support vector kernel methodsijcsit
Load Forecasting is powerful tool to make important decisions such as to purchase and generate the
electric power, load switching, development plans and energy supply according to the demand. The
important factors for forecasting involve short, medium and long term forecasting. Factors in short term
forecasting comprises of whether data, customer classes, working, non-working days and special event
data, while long term forecasting involves historical data, population growth, economic development and
different categories of customers.In this paper we have analyzed the load forecasting data collected from
one grid that contain the load demands for day and night, special events, working and non-working days
and different hours in day. We have analyzed the results using Machine Learning techniques, 10 fold cross
validation and stratified CV. The Machines Learning techniques used are LDA, QDA, SVM Polynomial,
Gaussian, HRBF, MQ kernels as well as LDA and QDA. The errors methods employed against the
techniques are RSE, MSE, RE and MAPE as presented in the table 2 below. The result calculated using the
SVM kernel shows that SVM MQ gives the highest performance of 99.53 %.
Electric Load Forecasting Using Genetic Algorithm – A Review IJMER
Many real-world problems from operations research and management science are very
complex in nature and quite hard to solve by conventional optimization techniques. So, intelligent
solutions based on genetic algorithm (GA), to solve these complicated practical problems in various
sectors are becoming more and more widespread nowadays. GAs are being developed and deployed
worldwide in myriad applications, mainly because of their symbolic reasoning, flexibility and
explanation capabilities.
This paper provides an overview of GAs, as well as their current use in the field of electric load
forecasting. The types of GA are outlined, leading to a discussion of the various types and parameters of
load forecasting. The paper concludes by sharing thoughts and estimations on GA for load forecasting
for future prospects in this area. This review reveals that although still regarded as a novel
methodology, GA technologies are shown to have matured to the point of offering real practical benefits
in many of their applications.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Electrical load forecasting through long short term memoryIJEECSIAES
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a
large amount of electrical energy cannot be stored. For the proper
functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis,
yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM)
neural networks, a recurrent neural network capable of handling both longterm and short-term dependencies of data sets, for predicting load that is to
be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.
Electrical load forecasting through long short term memorynooriasukmaningtyas
For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as a large amount of electrical energy cannot be stored. For the proper functioning of a power supply system, an adequate model for predicting load is a necessity. In the present world, in almost every industry, whether it be healthcare, agriculture, and consulting, growing digitization and automation is a prominent feature. As a result, large sets of data related to these industries are being generated, which when subjected to rigorous analysis, yield out-of-the-box methods to optimize the business and services offered. This paper aims to ascertain the viability of long short term memory (LSTM) neural networks, a recurrent neural network capable of handling both long-term and short-term dependencies of data sets, for predicting load that is to be met by a Dispatch Center located in a major city. The result shows appreciable accuracy in forecasting future demand.
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
Electrical Substation and Switchyard DesignLiving Online
Electrical substations form important nodal points in all power networks. Substations can be of various capacities, voltages, configurations and types depending on what is the application for which the substation is being designed. Location and layout of a substation present a number of challenges to the designer due to a large variety of options available to a designer. There are ever so many constraints too that need to be kept in mind; technical, environmental and naturally financial. Arriving at an optimum design within these constraints is as much an art as it is a science. Designing a substation which will operate with utmost reliability for at the least three or four decades involves a thorough knowledge of the current state-of-the art equipment, emerging technologies, the tools for presenting and evaluating all available options and a good appreciation of power system operation and maintenance. This course will present a comprehensive capsule of all the knowledge essential for a substation designer and walk the participants through the substation design process using a set of interlinked case studies.
FOR MORE INFORMATION: http://www.idc-online.com/content/electrical-substation-and-switchyard-design-25
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Thesis Statement for students diagnonsed withADHD.ppt
LOAD FORECASTING.ppt
1. Load Forecasting
• For example, for a particular region, it is possible to predict
the next day load with an accuracy of approximately 1-3%.
However, it is impossible to predict the next year peak load
with the similar accuracy since accurate long-term weather
forecasts are not available. For the next year peak forecast,
it is possible to provide the probability distribution of the
load based on historical weather observations. It is also
possible, according to the industry practice, to predict the
so-called weather normalized load, which would take place
for average annual peak weather conditions or worse than
average peak weather conditions for a given area. Weather
normalized load is the load calculated for the so-called
normal weather conditions which are the average of the
weather characteristics for the peak historical loads over a
certain period of time. The duration of this period varies
from one utility to another.
2. Load Forecasting
• With supply and demand .fluctuating and the changes of weather
conditions and energy prices increasing by a factor often or more
during peak situations, load forecasting is vitally important for
utilities.
• Short-term load forecasting can help to estimate load flows and to
make decisions that can prevent overloading. Timely
implementations of such decisions lead to the improvement of
network reliability and to the reduced occurrences of equipment
failures and blackouts.
• Load forecasting is also important for contract evaluations and
evaluations of various sophisticated financial products on energy
pricing offered by the market.
• In the deregulated economy, decisions on capital expenditures
based on long-term forecasting are also more important than in a
non-deregulated economy when rate increases could be justified
by capital expenditure on projects.
3. Factors for Load Forecasting
Most forecasting methods use statistical techniques or
artificial intelligence algorithms such as regression, neural
networks, fuzzy logic, and expert systems.
Medium & Long-term forecasting
Historical load
Weather data,
the number of customers in different categories
the appliances in the area and their characteristics including
age, the economic and demographic data and their
forecasts, the appliance sales data, and other factors.
Short-term forecasting.
time factors,
weather data,
possible customers’ classes.
4. Factors for Load Forecasting
The time factors include the time of the year, the day of the
week, and the hour of the day. There are important
differences in load between weekdays and weekends. The
load on different weekdays also can behave differently. For
example, Mondays and Fridays being adjacent to weekends,
may have structurally different loads than Tuesday through
Thursday. This is particularly true during the summer time.
Holidays are more difficult to forecast than non-holidays
because of their relative infrequent occurrence.
5. Factors for Load Forecasting
Weather conditions influence the load. In fact, forecasted
weather parameters are the most important factors for load
forecasting.
Most commonly used load predictors
Temperature and humidity
Among the weather variables listed above, two composite
weather variable functions,
the THI (temperature-humidity index) and
the WCI (wind chill index),
are broadly used by utility companies.
THI is a measure of summer heat discomfort
WCI is cold stress in winter.
Most electric utilities serve customers of different types such as residential,
commercial, and industrial. The electric usage pattern is different for customers
that belong to different classes but is somewhat alike for customers within each
class. Therefore, most utilities distinguish load behavior on a class-by-class basis
6. Methods of Load Forecasting
Most forecasting methods use statistical techniques or
artificial intelligence algorithms such as regression, neural
networks, fuzzy logic, and expert systems.
for medium- and long-term forecasting
End-use and econometric approach are broadly used
for short-term forecasting.
Similar day approach,
Various Regression models, Time series, Neural
networks,
Statistical learning algorithms, Fuzzy logic, Expert
systems
7. Methods of Load Forecasting
Statistical approaches usually require a mathematical model
that represents load as function of different factors such as
time, weather, and customer class.
The two important categories of such mathematical
models are:
additive models
multiplicative models.
They differ in whether the forecast load is
the sum (additive) of a number of components
the product (multiplicative) of a number of factors.
8. Methods of Load Forecasting
An additive model that takes the form of predicting load as the
function of four components:
= Ln + Lw + Ls + Lr,
where L is the total load,
Ln represents the “normal” part of the load, which is a set of
standardized load shapes for each “type” of day that has been
identified as occurring throughout the year,
Lw represents the weather sensitive part of the load,
Ls is a special event component that create a substantial
deviation from the usual load pattern,
Lr is a completely random term, the noise.
It is also suggested electricity pricing as an additional term
that can be included in the model. Naturally, price increases
/decreases affect electricity consumption. Large cost sensitive
industrial and institutional loads can have a significant effect
on loads.
9. Methods of Load Forecasting
A multiplicative model may be of the form
L = Ln · Fw · Fs · Fr,
Ln is the normal (base) load and the correction factors
Fw, Fs, and Fr are positive numbers that can increase or
decrease the overall load.
These corrections are based on current weather (Fw),
special events (Fs),and random fluctuation (Fr). Factors
such as electricity pricing (Fp) and load growth (Fg) can also
be included.
10. Methods of Load Forecasting
The end-use modeling, econometric modeling, and their
combinations are the most often used methods for medium-
and long-term load forecasting.
Descriptions of appliances used by customers, the sizes
of the houses,
the age of equipment, technology changes,
customer behavior, population dynamics are usually
included in the statistical and simulation models based
on the so-called end-use approach.
In addition, economic factors such as per capita incomes,
employment levels, and electricity prices are included in
econometric models. These models are often used in
combination with the end-use approach. Long-term forecasts
include the forecasts on the population changes, economic
development, industrial construction, and technology
development.
Medium & Long Term Load Forecasting
11. Methods of Load Forecasting
End-use models
The end-use approach directly estimates energy
consumption by using extensive information on end use and
end users, such as appliances, the customer use, their age,
sizes of houses, and so on.
Statistical information about customers along with dynamics
of change is the basis for the forecast.
End-use models focus on the various uses of electricity in
the residential, commercial, and industrial sector. These
models are based on the principle that electricity demand is
derived from customer’s demand for light, cooling, heating,
refrigeration, etc. Thus end-use models explain energy
demand as a function of the number of appliances in the
market
sensitive to the amount and quality of end-use data. For example, in this
method the distribution of equipment age is important for particular types
of appliances. End-use forecast requires less historical data but more
information about customers and their equipment.
Medium & Long Term Load Forecasting
12. Methods of Load Forecasting
Econometric models.
The econometric approach combines economic
theory and statistical techniques for forecasting electricity
demand.
The approach estimates the relationships between energy
consumption (dependent variables) and factors influencing
consumption.
The relationships are estimated by the least-squares method
or time series methods.
One of the options in this framework is to aggregate the
econometric approach, when consumption in different
sectors (residential, commercial, industrial, etc.) is calculated
as a function of weather, economic and other variables, and
then estimates are assembled using recent historical
data.
Integration of the econometric approach into the end-use
approach introduces behavioral components into the end-
use equations.
Medium & Long Term Load Forecasting
13. Methods of Load Forecasting
Statistical model-based learning
The end-use and econometric methods require a large
amount of information relevant to appliances, customers,
economics, etc. Their application is complicated and
requires human participation. In addition such
information is often not available regarding particular
customers and a utility keeps and supports a profile of
an “average” customer or average customers for
different type of customers. The problem arises if the
utility wants to conduct next-year forecasts for sub-
areas, which are often called load pockets. In order to
simplify the medium-term forecasts, make them more
accurate and avoid the use of the unavailable
information.
14. Methods of Load Forecasting
The focus of the study was the summer data. We compared
several load models and came to the conclusion that the
following multiplicative model is
the most accurate
L(t) = F(d(t), h(t)) · f(w(t)) + R(t),
where L(t) is the actual load at time t,
d(t) is the day of the week,
h(t) is the hour of the day,
F(d, h) is the daily and hourly component,
w(t) is the weather data that include the temperature and
humidity,
f(w) is the weather factor, and R(t) is a random error.
In fact, w(t) is a vector that consists of the current and lagged
weather variables. This reflects the fact that electric load
depends not only on the current weather conditions but also
on the weather during the previous hours and days. In
particular, the well-known effect of the so-called heat waves
is that the use of air conditioners increases when the hot
15.
16.
17. Methods of Load Forecasting
Methods of Short Term Load forecasting
Similar-day approach
Regression methods
Time series
Neural networks
Expert systems
Fuzzy logic.
Short Term Load Forecasting
18. Methods of Load Forecasting
Similar-day approach
This approach is based on searching historical data for days
within one, two, or three years with similar characteristics
to the forecast day.
Similar characteristics include weather, day of the week, and
the date. The load of a similar day is considered as a
forecast.
Instead of a single similar day load, the forecast can be a
linear combination or regression procedure that can include
several similar days.
The trend coefficients can be used for similar days in the
previous years.
Short Term Load Forecasting
19. Methods of Load Forecasting
Regression methods
used to model the relationship of load consumption and
other factors such as weather, day type, and customer class.
several regression models for the next day peak forecasting.
Their models incorporate deterministic influences such as
holidays, stochastic influences such as average loads, and
exogenous influences such as weather.
Short Term Load Forecasting
20. Methods of Load Forecasting
Time series
Time series methods are based on the assumption that
the data have an internal structure, such as autocorrelation,
trend, or seasonal variation. Time series forecasting
methods detect and explore such a structure. Time series
have been used for decades in such fields as economics,
digital signal processing, as well as electric load forecasting.
Short Term Load Forecasting
21. Methods of Load Forecasting
Time series
ARMA (autoregressive moving average),
ARIMA (autoregressive integrated moving average),
ARMAX (autoregressive moving average with exogenous
variables),
ARIMAX (autoregressive integrated moving average with
exogenous variables) are the most often used classical time
series methods.
ARMA models are usually used for stationary processes while
ARIMA is an extension of ARMA to nonstationary
processes.
ARMA and ARIMA use the time and load as the only input
parameters. Since load generally depends on the weather and
time of the day,
ARIMAX is the most natural tool for load forecasting among
Short Term Load Forecasting
22. Methods of Load Forecasting
Neural networks
The use of artificial neural networks (ANN) has been a
widely studied electric load forecasting technique since
1990.
Neural networks are essentially non-linear circuits that
have the demonstrated capability to do non-linear curve
fitting.
The outputs of an artificial neural network are some linear or
nonlinear mathematical function of its inputs. The inputs may
be the outputs of other network elements as well as actual
network inputs.
In practice network elements are arranged in a relatively
small number of connected layers of elements between
network inputs and outputs. Feedback paths are sometimes
used.
Short Term Load Forecasting
23. Methods of Load Forecasting
Neural networks
In applying a neural network to electric load forecasting, one
must select one of a number of architectures (e.g. Hopfield,
back propagation, Boltzmann machine), the number and
connectivity of layers and elements, use of bi-directional or
uni-directional links, and the number format (e.g. binary or
continuous) to be used by inputs and outputs,and internally.
The most popular artificial neural network architecture for
electric load forecasting is back propagation.
Back propagation neural networks use continuously valued
functions and supervised learning.
Short Term Load Forecasting
24. Methods of Load Forecasting
Expert systems
Rule based forecasting makes use of rules, which are
often heuristic in nature, to do accurate forecasting.
Expert systems incorporates rules and procedures used by
human experts in the field of interest into software that is
then able to automatically make forecasts without human
assistance.
This rule base is complemented by a parameter database
that varies from site to site.
Short Term Load Forecasting
25. Methods of Load Forecasting
Fuzzy logic
Fuzzy logic is a generalization of the usual Boolean logic
used for digital circuit design. An input under Boolean logic takes on
a truth value of “0” or “1”.
Under fuzzy logic an input has associated with it a certain
qualitative ranges.
For instance a transformer load may be “low”, “medium” and “high”.
Fuzzy logic allows one to (logically) deduce outputs from fuzzy
inputs. In this sense fuzzy logic is one of a number of techniques for
mapping inputs to outputs (i.e. curve fitting).
Among the advantages of fuzzy logic are the absence of a need for
a mathematical model mapping inputs to outputs and the absence
of a need for precise (or even noise free) inputs.
Short Term Load Forecasting
26. Methods of Load Forecasting
The development and improvements of appropriate
mathematical tools will lead to the development of more
accurate load forecasting techniques. The accuracy of load
forecasting depends not only on the load forecasting
techniques, but also on the accuracy of forecasted weather
scenarios.