A Proposed Hybrid Deep Learning Model for Wind Power Forecasting

Abstract

Renewable energy forecasting is crucially important because of its fluctuation and stochastic characteristics. In this paper, a hybrid model for wind speed and power forecasting using neuro wavelet and long short-term memory (LSTM) is proposed. The architecture of the proposed forecasting model involves two steps; the first step is to employ a time-based neuro wavelet for the wind speed or power forecasting. The second step is to subtract the forecasted wind speed or power from the actual ones to calculate the error (residuals). This error is then fed as an input to the LSTM to determine the forecasted wind speed or power error. The forecasted wind speed will be equal to that from the first step and the forecasted wind error from the second step. The same procedures are repeated for the forecasted wind power. In this paper, a simulated model for wind power is used. The results demonstrate the effectiveness of the proposed model for wind speed and power forecasting.