Wavelet Based Spike Propagation Neural Network (WSPNN) for Wind Power Forecasting

Smitha Elsa Peter(1*), Santosh Kulkarni(2), I. Jacob Raglend(3), Sishaj P. Simon(4)

(1) PRIST University, Thanjavur, India
(2) Jawaharlal Nehru Technological University Hyderabad and National Institute of Technology Tiruchirappalli, India
(3) of Electrical Sciences, Noorul Islam University, Kumaracoil, Kanyakumari, Tamil Nadu, India
(4) Institute of Technology (NIT) (formerly Regional Engineering College), Tiruchirappalli, Tamil Nadu, India
(*) Corresponding author


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Abstract


The development of wind power generation in many countries around the world in recent years has drawn the attention of research scientists to forecast wind power. The characteristics of the wind power generated are extremely variable, non-periodic and unpredictable. Therefore, wind power forecasts have gained a lot of prominence and its significance in turbine control, pre-load sharing and trading of power. Based on the literature survey, though various wind power forecasting models have been proposed, scope for developing much more efficient and accurate models is still a major challenge. This work proposes a wavelet based spike propagation neural network for wind power forecasting. First the preprocessing of historical data is carried out using wavelet technique.  Then, the third generation artificial neural networks, that involve the spike response neuron model (SRNM) is trained using the gradient descent approach. The proposed model is tested on the historical wind power data obtained from Irish power grid and National Renewable Energy Laboratory (USA) website. The performance of the proposed model is validated based on the indices such as modified mean absolute percentage error (MMAPE), root mean square error (RMSE) and error variance.
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Keywords


Artificial Neural Networks; Spike Response Model (SRM); Spiking Neurons; Wind Power forecasting (WPF)

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