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FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FOR DETERMININGHYDROGEN PRODUCTION POTENTIAL
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FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FOR DETERMINING HYDROGEN PRODUCTION POTENTIAL

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My topic of thesis is about constructing prediction model of wind energy and solar energy by artificial neural network with genetic algorithm. ...

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.

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FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FOR DETERMININGHYDROGEN PRODUCTION POTENTIAL FORECASTING OF RENEWABLE ENERGY PRODUCTION BY USING GENETIC ALGORITHM (GA) FOR DETERMINING HYDROGEN PRODUCTION POTENTIAL Presentation Transcript

  • 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
  • Outline
    • Introduction
    • Hybrid system : Wind and PV
    • Forecasting
    • Comparison
    • Model and process
    • Conclusion & future work
    • Q&A
  • Introduction
    • Why Renewable Energy Production?
    • Wind power & PV combination
    • Unstable output in electricity market
    • Benefit of optimum sizing and forecasting
    • Purpose
      • Optimum sizing
      • Forecasting of RE energy
  • Introduction (cont.)
    • Optimum Sizing
      • Optimum sizing combination of a battery bank and PV array
      • Used a stochastic analytical model to optimize a wind power plant
      • Generation of unit sizing hybrid system
      • Using GA to optimize renewable generating systems
  • Introduction (cont.)
    • Optimum forecasting
      • A rtificial N eural N etwork (ANN)
      • Applied neural network in prediction and identification
      • Applied GA based ANN for short term load forecasting
      • Applied ANN to identify wind energy output
  • Introduction (cont.)
    • Optimum forecasting
      • F uzzy L ogic (FL)
      • Applied fuzzy in predicting chaotic time series
      • Applied genetic Fuzzy predictor in forecasting time series problem
  • Hybrid System
    • In 2000, K. Agbossou et al. proposed the stand-alone hybrid system
  • Hybrid System (cont.)
    • Modified hybrid system to supply fuel cell and connected to the electricity net
  • Forecasting
    • Forecasting :
      • to precisely predict the short-term evolution of the system n
  • Forecasting (cont.)
    • Multiple regression
    • Exponential smoothing
    • Iterative reweighted least squares
    • Adaptive load forecasting
    • Stochastic time series
    • ARMAX model based on GA
    • Artificial Neural Network (ANN)
    • Fuzzy Logic (FL)
    • Expert system
    • Begin
      • General theories of learning, vision, conditioning
      • No specific mathematical models of neuron operation
    • Develop
      • Mechanism for learning in biological neurons
      • Neural-like networks can compute any arithmetic function
    • Application
      • First practical networks and learning rules
    Artificial Neural Network
  • Artificial Neural Network (cont.)
    • Supervised
    • Unsupervised
  • Note: All you need to construct the functional model are the discrete data points. Artificial Neural Network (cont.)
  • Fuzzy Logic
    • Fuzzy set theory by L. A. Zadeh
    IF THEN INFERRED Combined& Defuzzified T T V V V V 0.2 0.8 Defuzzified output T=80 input R 1 R 2
  • Genetic Algorithm
    • “ The Origin of Species by Means of Natural Selection” by Charles Darwin
    • Dr. John Holland etc. proposed GA
      • Approach “Global Optimum”
  • Genetic Algorithm (cont.)
    • Gene:
    • Processes:
  • Genetic Algorithm (cont.)
    • Process:
      • Reproduction:
      • Crossover: two parents / two offspring
      • Mutation : random alteration of some gene values in an individual.
    parents offspring
    • ANN & GA
    Model and Process
  • Model and Process
    • FL & GA
    • Accuracy
    Comparison Mean square error of ANN prediction in [1]
  • Table 1. Comparison results of RMSE of various forecasting models [4]
    • Efficiency
    Comparison (cont.) 0.007 500 ANFIS 0.06 500 Cascade Correlation NN 0.55 500 Linear Predictive Method 0.0907 500 Product operator 0.09 500 Min operator 0.19 500 Auto Regressive Model 0.04 500 6th-order Polynomial 0.02 500 BP ANN 0.038011 (9 partition) 500 FL&GA Prediction Error (RMSE) Training Method
    • Modified hybrid system
    • Modified hybrid system process
      • Wind energy
    • Modified hybrid system process
      • PV array
    • Modified hybrid system process
      • Sizing
  • Conclusions
    • ANN model perform better in forecasting
      • Accuracy
      • Efficiency
    • ANN with GA get better result
    • Hybrid system could no longer be affected
    • Utilize GA to get optimum sizing
    • Applicability used in other RE sources
  • Q&A
    • Thank you for your attention.