HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting

Abstract

The forecasting and estimation of wind power is a challenging problem in renewable energy generation due to the high volatility of wind power resources, inevitable intermittency, and complex fluctuation. In recent years, deep learning techniques, especially recurrent neural networks (RNN), showed prominent performance in time-series forecasting and prediction applications. One of the main efficient RNNs is the long short term memory (LSTM), which we adopted in this study to forecast the wind power from different wind turbines. We adopted the advances of the metaheuristic optimization algorithms to train the LSTM and to boost its performance by optimizing its parameters. The Heap-based optimizer (HBO) is a new human-behavior-based metaheuristic algorithm that was inspired by corporate rank hierarchy, and it was employed to solve complex optimization and engineering problems. In this study, HBO is used to train the LSTM, and it showed significant enhancement on the LSTM prediction performance. We used four datasets from the well-known wind turbines in France, La Haute Borne wind turbines, to evaluate the developed HBO-LSTM. We also considered several optimized LSTM models using several optimization algorithms for comparisons, as well as several existing models. The comparison outcome confirmed the capability of HBO to boost the prediction performance of the basic LSTM model.