Wind energy potential estimation using neural network and SVR approaches
Copyright policy )Wind energy potential estimation using neural network and SVR approaches
The distribution of wind speed and the optimal assessment of wind energy potential are very important factors when selecting a suitable site for a wind power plant. In wind farm design projects for the supply of electrical energy, designers use the Weibull distribution law to analyse the characteristics and variations of wind speed in order to evaluate the wind potential. In our study we used two approaches, namely, the Multilayer Perceptron (MLP) approach and the Support Vector Machine (SVR) approach to determine a distribution law of wind speeds and to optimally evaluate the wind potential. These two approaches were compared to two well-known numerical methods which are the Justus Empirical Method (EMJ) and the Maximum Likelihood Method (MLM). The results show that the neural network approach produces a better fit of the distribution curve with an Root Mean Square Error (RMSE) of 0.00005016 at Lomé, 0.000040289 at Cotonou site and a more interesting estimate of the wind potential. After that SVR show a better result too with an RMSE of 0.0095618 at the Lomé site and 0.0053549 at the Cotonou site.
- University of Lomé Togo
Artificial neural network, Artificial intelligence, Environmental Engineering, Support vector machine, Electricity Price and Load Forecasting Methods, Aerospace Engineering, FOS: Mechanical engineering, Wind Power Generation, Wind speed, neural network; support vector regression; multilayer perceptron; wind energy; weibull distribution, Engineering, Meteorology, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Electrical and Electronic Engineering, Electricity Price Forecasting, Geography, Statistics, Urban Wind Environment and Air Quality Modeling, FOS: Environmental engineering, Load Forecasting, Computer science, Wind Farm Optimization, Electrical engineering, Physical Sciences, Environmental Science, Mean squared error, Weibull distribution, Wind Energy Technology and Aerodynamics, Wind power, Short-Term Forecasting, Mathematics
Artificial neural network, Artificial intelligence, Environmental Engineering, Support vector machine, Electricity Price and Load Forecasting Methods, Aerospace Engineering, FOS: Mechanical engineering, Wind Power Generation, Wind speed, neural network; support vector regression; multilayer perceptron; wind energy; weibull distribution, Engineering, Meteorology, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Electrical and Electronic Engineering, Electricity Price Forecasting, Geography, Statistics, Urban Wind Environment and Air Quality Modeling, FOS: Environmental engineering, Load Forecasting, Computer science, Wind Farm Optimization, Electrical engineering, Physical Sciences, Environmental Science, Mean squared error, Weibull distribution, Wind Energy Technology and Aerodynamics, Wind power, Short-Term Forecasting, Mathematics
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