Deep learning based multi-step short term wind speed forecasts with LSTM

Abstract

Wind energy is one of the fastest progressing renewable energy sources. Wind speed forecasting has gained more attention as wind energy never causes air pollution and other hazards to the environment. Eco-friendliness and cost-effectiveness are the main reasons for the high demand for wind energy. Accurate and reliable prediction model for wind speed forecasting is very challenging because of its intermittent nature. Deep learning based architectures like Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) are considered as better models for time series prediction. LSTM neural network models are more appropriate for learning long term dependencies. In this paper, an LSTM network for a multi-step-ahead short term wind speed forecasting is proposed. As lag values have importance in time series prediction problems, tests are conducted to find optimal lag value. The value which contributes minimum mean square error is selected as optimal lag value. The study of the results reveals that the proposed LSTM model is more effective and efficient with high predictive accuracy compared to Auto-Regressive Integrated Moving Average (ARIMA) model.