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Hybrid Neural Networks for Renewable Energy Forecasting: Solar and Wind Energy Forecasting Using LSTM and RNN
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Author(s): Firuz Ahamed Nahid (Asian Institute of Technology, Thailand), Weerakorn Ongsakul (Asian Institute of Technology, Thailand), Nimal Madhu M. (Asian Institute of Technology, Thailand)and Tanawat Laopaiboon (Asian Institute of Technology, Thailand)
Copyright: 2021
Pages: 23
Source title:
Research Advancements in Smart Technology, Optimization, and Renewable Energy
Source Author(s)/Editor(s): Pandian Vasant (University of Technology Petronas, Malaysia), Gerhard Weber (Poznan University of Technology, Poland)and Wonsiri Punurai (Mahidol University, Thailand)
DOI: 10.4018/978-1-7998-3970-5.ch011
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Abstract
One of the key applications of AI algorithms in power sector involves forecasting of stochastic renewable energy sources. To manage the generation of electricity from solar or wind effectively, accurate forecasting models are imperative. In order to achieve this goal, a sophisticated hybrid neural network formulation is discussed here in this chapter. long-short-term memory and recurrent neural networks combination is formulated for very short-term forecasting of wind speed and solar radiation. In intervals of 15 and 30 minutes, time series forecasts are made that are ahead by multiple steps. For maximum energy harvest, both point wise and probabilistic forecasting approaches are combined. Historic data is collected for solar radiation, wind speed, temperature, and relative humidity, and are used to train the model. The proposed model is compared with convolutional and LSTM neural network models individually in terms of RMSE, MAPE, MAE, and correlation, and is identified to have better forecasting accuracy.
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