Wind speed prediction based on longshort term memory using nonlinear autoregressive neural networks

Abstract

Globally, wind power is a technologically matured and commercially accepted technology. However, intermittent and fluctuating wind speed makes it difficult to connect it directly to the grid. It becomes less attractive from the quality and continuous power supply point of view. Nevertheless, the wind speed is affected by meteorological parameters like temperature, pressure, and relative humidity and may be predicted better using all of these parameters or some of the theses as inputs. Since the weather conditions of a particular month repeat approximately after ten years and sometimes even year to year depending on geographical location. This study investigates the errors associated with predicting the wind speed of a particular calendar month using the historical data of the same calendar month in the previous years. Authors propose a strategy for long-term prediction of wind speed based on two nonlinear autoregressive neural network models, (i) nonlinear autoregressive neural network and (ii) nonlinear autoregressive neural networks with exogenous inputs. The models are developed by training the networks with hourly mean wind speed values for seven years, from 2011 to 2017, for three sites in the Eastern Province of Saudi Arabia. These models are used to predict the wind speed for 2018, and the results are compared with the measured data. Both models' effectiveness is evaluated by considering the impact of the exogenous parameters (temperature and atmospheric pressure). The study found that the prediction accuracy of wind speed in long-term forecasting depends not only on the location but also on the repeatability of training samples across the years.