My topic of thesis is about constructing prediction model of wind energy and solar energy by artificial neural network with genetic algorithm.
Because the electricity price in Nordic area is determined in advance, it is beneficial to construct prediction model for renewable energy. With the mentioned prediction model, we can construct hybrid system (wind energy and solar energy) to produce hydrogen. Then the fuel cell can produce electricity and connect to the network by using the constructed prediction model. In our prediction model, the critical parameters like 10 minutes average wind speed and deviation for wind energy could be used as historical data to train to artificial neural network (ANN). We also hope to utilize genetic algorithm (GA) to speed up the ANN training and hybrid system scale design.
Comparison of Photovoltaic Array Maximum Power Point Tracking TechniquesAsoka Technologies
The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.
Application of artificial neural networks for shunt active power filter controlAsoka Technologies
Artificial neural network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN-based control algorithms for shunt APF. A step-by-step procedure to implement these ANN-based techniques in MATLAB/Simulink environment is provided. Furthermore, a detailed analysis on the performance, limitation, and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.
VTT:n mukaan tuuliennusteiden virheistä aiheutuvat kustannukset voidaan jopa puolittaa, kun tuulivoimaennusteet tehdään useille maantieteellisesti hajautetuille tuulipuistoille yksittäisten tuulipuistojen sijasta. VTT on myös selvittänyt tuotantovaihtelujen tasaantumista Pohjoismaiden alueella. Tuloksia voidaan hyödyntää säätövoimatarpeen suunnittelussa ja tuulivoiman vaihtelun ennustamisessa.
Comparison of Photovoltaic Array Maximum Power Point Tracking TechniquesAsoka Technologies
The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.
Application of artificial neural networks for shunt active power filter controlAsoka Technologies
Artificial neural network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN-based control algorithms for shunt APF. A step-by-step procedure to implement these ANN-based techniques in MATLAB/Simulink environment is provided. Furthermore, a detailed analysis on the performance, limitation, and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.
VTT:n mukaan tuuliennusteiden virheistä aiheutuvat kustannukset voidaan jopa puolittaa, kun tuulivoimaennusteet tehdään useille maantieteellisesti hajautetuille tuulipuistoille yksittäisten tuulipuistojen sijasta. VTT on myös selvittänyt tuotantovaihtelujen tasaantumista Pohjoismaiden alueella. Tuloksia voidaan hyödyntää säätövoimatarpeen suunnittelussa ja tuulivoiman vaihtelun ennustamisessa.
Matlab reversible data hiding with optimal value transferEcway Technologies
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...Kishor Datta Gupta
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this project to find the optimal solution of energy scheduling throughout a day, simultaneously using the concept of network reconfiguration. In this research paper, we have applied a genetic algorithm approach, in order to optimize dispatching power with reconfiguring the network and scheduling the power sources. Our proposed methods shows that, it is possible to get 1MW less line lose compared to general condition.
Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...IJERA Editor
Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy
Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Powe...del2infinity Energy
A Technical paper on ‘Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Power in India’’ by Abhik Kumar Das at India SMART GRID Week 2017 organised by India Smart Grid Forum & Government of India at Manekshaw Centre, New Delhi.
ENERGY-EFFICIENT SCHEDULING OF VIRTUAL MACHINES USING ANT COLONY OPTIMIZATIO...Abhishek Panda
This poster shows the usage of ant colony optimization techniques and genetic operators for energy aware scheduling of VM's in the physical servers of a data centre
The operational scale of edge computing introduces new challenges for building and operating suitable computation platforms. This talk was given at SEC'18 (http://acm-ieee-sec.org/2018/) and reports on the paper 'Portable Energy-Aware Cluster-Based Edge Computers'.
Matlab reversible data hiding with optimal value transferEcway Technologies
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB Projects in Salem, 2013 IEEE MATLAB Projects in Coimbatore, 2013 IEEE MATLAB Projects in Tirupur, 2013 IEEE MATLAB Projects in Bangalore, 2013 IEEE MATLAB Projects in Hydrabad, 2013 IEEE MATLAB Projects in Kerala, 2013 IEEE MATLAB Projects in Namakkal, IEEE MATLAB Image Processing, IEEE MATLAB Face Recognition, IEEE MATLAB Face Detection, IEEE MATLAB Brain Tumour, IEEE MATLAB Iris Recognition, IEEE MATLAB Image Segmentation, Final Year Matlab Projects in Pondichery, Final Year Matlab Projects in Tamilnadu, Final Year Matlab Projects in Chennai, Final Year Matlab Projects in Trichy, Final Year Matlab Projects in Erode, Final Year Matlab Projects in Karur, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Tirunelveli, Final Year Matlab Projects in Madurai, Final Year Matlab Projects in Salem, Final Year Matlab Projects in Tirupur, Final Year Matlab Projects in Namakkal, Final Year Matlab Projects in Tanjore, Final Year Matlab Projects in Coimbatore, Final Year Matlab Projects in Bangalore, Final Year Matlab Projects in Hydrabad, Final Year Matlab Projects in Kerala.
A Genetic Algorithm Approach to Optimize Dispatching for A Micro-grid Energy ...Kishor Datta Gupta
Distributed network reconfiguration techniques are used widely to optimize power distribution systems. As renewable energy generation are very stochastic in nature, network reconfiguration with this stochastic nature does not provide the optimal solution. To address this problem a three-objective genetic algorithm approach has been taken in this project to find the optimal solution of energy scheduling throughout a day, simultaneously using the concept of network reconfiguration. In this research paper, we have applied a genetic algorithm approach, in order to optimize dispatching power with reconfiguring the network and scheduling the power sources. Our proposed methods shows that, it is possible to get 1MW less line lose compared to general condition.
Implementing Workload Postponing In Cloudsim to Maximize Renewable Energy Uti...IJERA Editor
Green datacenters has become a major research area among researchers in academy and industry. One of the
recent approaches getting higher attention is supplying datacenters with renewable sources of energy, leading to
cleaner and more sustainable datacenters. However, this path poses new challenges. The main problem with
existing renewable energy technologies is high variability, which means high fluctuation of available energy
during different time periods on a day, month or year. In our paper, we address the issue of better managing
datacenter workload in order to achieve higher utilization of available renewable energy. We implement an
algorithm in CloudSim simulator which decides to postpone or urgently run a specific job asking for datacenter
resources, based on job’s deadline and available solar energy. The aim of this algorithm is to make workload
energy consumption through 24 hours match as much as possible the solar energy availability in 24 hours. Two
typical, clear and cloudy days, are taken in consideration for simulation. The results from our experiments show
that, for the chosen workload model, jobs are better managed by postponing or urgently running them, in terms
of leveraging available solar energy. This yields up to 17% higher utilization of daily solar energy
Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Powe...del2infinity Energy
A Technical paper on ‘Applicability of Error Limit in Forecasting & Scheduling of Wind & Solar Power in India’’ by Abhik Kumar Das at India SMART GRID Week 2017 organised by India Smart Grid Forum & Government of India at Manekshaw Centre, New Delhi.
ENERGY-EFFICIENT SCHEDULING OF VIRTUAL MACHINES USING ANT COLONY OPTIMIZATIO...Abhishek Panda
This poster shows the usage of ant colony optimization techniques and genetic operators for energy aware scheduling of VM's in the physical servers of a data centre
The operational scale of edge computing introduces new challenges for building and operating suitable computation platforms. This talk was given at SEC'18 (http://acm-ieee-sec.org/2018/) and reports on the paper 'Portable Energy-Aware Cluster-Based Edge Computers'.
Machine Learning Techniques for the Smart Grid – Modeling of Solar Energy usi...Wilfried Elmenreich
This talk covers the application of machine learning techniques for energy applications, in particular for modeling solar radiation. The first part explores meta-heuristic search algorithms and envisioned their application for designing distributed, self-organizing control systems using evolutionary algorithms. The second part gives an introduction to solar radiation modeling and shows how neural networks can be used to artificial neural networks to learn the correlation of input parameters such as latitude, longitude, temperature, humidity, month, day, hour to predict global and diffuse solar radiation.
Wind Energy in the southern Mediterranean, the case of EgyptPARIS
Report by Eng Bothaina Rached, General Manager of NREA, New and renewable energy authority, Cairo, www.nrea.gov.eg
Presented at the workshop of the Mediterranean Solar Plan in Berlin, 28&29 Oct 2008.
its a presentation on stock market analysis using Genetic algorithm with Neural networks ,based on a scientific paper
,made in Cairo university under Supervision of prof.Dr. Magda
Applying soft computing techniques to corporate mobile security systemsPaloma De Las Cuevas
Corporate workers increasingly use their own devices for work purposes, in a trend that has come to be called the "Bring Your Own Device" (BYOD) philosophy and companies are starting to include it in their policies. For this reason, corporate security systems need to be redefined and adapted, by the corporate Information Technology (IT) department, to these emerging behaviours. This work proposes applying soft-computing techniques, in order to help the Chief Security Officer (CSO) of a company (in charge of the IT department) to improve the
security policies.
The actions performed be company workers under a BYOD situation will be treated as events: an action or set of actions yielding to a response. Some of those events might cause a non compliance with some corporate policies, and then it would be necessary to define a set of security rules (action, consequence). Furthermore, the processing of the extracted knowledge will allow the rules to be adapted.
Optimization of distributed generation of renewable energy sources by intelli...Beniamino Murgante
Optimization of distributed generation of renewable energy sources by intelligent techniques
Marcello Pucci – Institute for Studies on Intelligent Systems for Automation (I.S.S.I.A), National Research Council, Palermo (Italy)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
A Comparison Study between Inferred State-Space and Neural Network Based Syst...Ahmed Momtaz Hosny, PhD
Abstract:
In this paper, system identifications of an unmanned aerial vehicle (UAV) based on inferred state space and multiple neural networks were presented. In this work an optimization approach was used to conclude an inferred state space and the multiple neural networks system identifications based on the genetic algorithms separately.
The presented work is focusing on an inferred state space based system identification which is a new approach seldom used, but it is also easier and more stable compared with the multi-network based system identification during the modeling of dynamic behavior of nonlinear systems.
Wind power forecasting an application of machineJawad Khan
The advancement in renewable energy sector being the focus of research these days, a novel neuro evolutionary technique is proposed for modeling wind power forecasters.
The work uses the robust technique of
Cartesian Genetic Programming to evolve ANN
for development of forecasting models.
These Models predicts power generation of a wind based power plant from a single hour up to a year - taking a big lead over other proposed models by reducing its MAPE to minimum values for a single day hourly prediction.
Results when compared with other models in the literature demonstrated that the proposed models are among the best estimators of wind based power generation plants proposed to date.
Optimal Power Generation in Energy-Deficient Scenarios Using Bagging EnsemblesKashif Mehmood
This paper presents an improved technique for optimal power generation using ensemble
artificial neural networks (EANN). The motive for using EANN is to benefit from multiple parallel processor
computing rather than traditional serial computation to reduce bias and variance in machine learning. The
load data is obtained from the load regulation authority of Pakistan for 24 hours. The data is analyzed on an
IEEE 30-bus test system by implementing two approaches; the conventional artificial neural network (ANN)
with feed-forward back-propagation model and a Bagging algorithm. To improve the training of ANN and
authenticate its result, first the Load Flow Analysis (LFA) on IEEE 30 bus is performed using Newton
Raphson Method and then the program is developed in MATLAB using Lagrange relaxation (LR) framework
to solve a power-generator scheduling problem. The bootstraps for the EANN are obtained through a disjoint
partition Bagging algorithm to handle the fluctuating power demand and is used to forecast the power
generation. The results of MATLAB simulations are analyzed and compared along with computational
complexity, therein showing the dominance of the EANN over the traditional ANN strategy that closed
to LR
Solar Photovoltaic Power Forecasting in Jordan using Artificial Neural NetworksIJECEIAES
In this paper, Artificial Neural Networks (ANNs) are used to study the correlations between solar irradiance and solar photovoltaic (PV) output power which can be used for the development of a real-time prediction model to predict the next day produced power. Solar irradiance records were measured by ASU weather station located on the campus of Applied Science Private University (ASU), Amman, Jordan and the solar PV power outputs were extracted from the installed 264KWp power plant at the university. Intensive training experiments were carried out on 19249 records of data to find the optimum NN configurations and the testing results show excellent overall performance in the prediction of next 24 hours output power in KW reaching a Root Mean Square Error (RMSE) value of 0.0721. This research shows that machine learning algorithms hold some promise for the prediction of power production based on various weather conditions and measures which help in the management of energy flows and the optimisation of integrating PV plants into power systems.
Optimal Power Flow with Reactive Power Compensation for Cost And Loss Minimiz...ijeei-iaes
One of the concerns of power system planners is the problem of optimum cost of generation as well as loss minimization on the grid system. This issue can be addressed in a number of ways; one of such ways is the use of reactive power support (shunt capacitor compensation). This paper used the method of shunt capacitor placement for cost and transmission loss minimization on Nigerian power grid system which is a 24-bus, 330kV network interconnecting four thermal generating stations (Sapele, Delta, Afam and Egbin) and three hydro stations to various load points. Simulation in MATLAB was performed on the Nigerian 330kV transmission grid system. The technique employed was based on the optimal power flow formulations using Newton-Raphson iterative method for the load flow analysis of the grid system. The results show that when shunt capacitor was employed as the inequality constraints on the power system, there is a reduction in the total cost of generation accompanied with reduction in the total system losses with a significant improvement in the system voltage profile
Power system transient stability margin estimation using artificial neural ne...elelijjournal
This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
Performance Evaluation of GA optimized Shunt Active Power Filter for Constant...ijeei-iaes
Sinusoidal Current Control strategy for extracting reference currents for shunt active power filters have been modified using Genetic Algorithm and its performances have been compared. The acute analysis of Comparison of the compensation capability based on THD and speedwell be done, and recommendations will be given for the choice of technique to be used. The simulated results using MATLAB model are shown, and they will undoubtedly prove the importance of the proposed control technique of aircraft shunt APF.
An IntelligentMPPT Method For PV Systems Operating Under Real Environmental C...theijes
The sun irradiance (G) and temperature (T) are the two main factors that affect the output power gained from the photovoltaic (PV) DC–DC converter. Therefore, to enhance the performance of the overall system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique, experience difficulty in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). The selection of the membership functions (MFs) and the fuzzy sets (FSs) numbers are crucial in the performance of the FLC based MPPT. Accordingly, this work presents numerous adaptive neuro-fuzzy systems to automatically adjustthe fuzzy logic controller membership functions as an alternative to the trial and error approach, which waste time and effort in MPPT design. For this purpose an adaptive neuro-fuzzy system is developed in MATLAB/Simulink to determine suitable MFs and the FSs for the fuzzy logic controller. The effects of different types of MFs and the FSs are deeply investigated using real data collected from the rooftop PV system. The investigations show that the fuzzy logic controller with a triangular membership function and seven fuzzy setsprovides the best results
Identification study of solar cell/module using recent optimization techniquesIJECEIAES
This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.
1.a fuzzy based pv apf controller for compensating current harmonics (2)EditorJST
The main aim of this paper is to compensate a current harmonics in PV-APF system using Fuzzy Logic Controller. A 3- Ф 3-wire system is proposed in this paper which consists of PV system, a dc/dc converter which is controlled by MPPT, three phase VSC to act as APF and Non-Linear Load. The main theme of this INC MPPT is to efficiency from the PV system. For reliable performance of active power filter and better harmonic compensation this paper propose a concept of instantaneous power theory. Also, a comparison analysis is performed for improving THD by PI/Fuzzy controllers. The proposed system is simulated and verified in MATLAB/SIMULINK software.
Improvement of grid connected photovoltaic system using artificial neural net...ijscmcj
Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.
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
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FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FOR DETERMINING HYDROGEN PRODUCTION POTENTIAL
1. FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FOR DETERMINING HYDROGEN PRODUCTION POTENTIAL University of Jyv äskylä Renewable Energy Master Programme Reporter : Lin Tzu Chao 24/11/2006
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