Deep Belief Networks With Genetic Algorithms in Forecasting Wind Speed - Kuo-Ping Lin; Ping-Feng Pai; Yi-Ju Ting (IEEE Access, 2019)
Wind is one of the most essential sources of clean, renewable energy, and is, therefore, a critical element in responsible power consumption and production. The accurate prediction of wind speed plays a key role in decision-making and management in wind power generation. This study proposes a model using a deep belief network with genetic algorithms (DBNGA) for wind speed forecasting. The genetic algorithms are used to determine parameters for deep belief networks. Wind speed and weather-related data are collected from Taiwan's central weather bureau for this purpose. This paper uses both time series data and multivariate regression data to forecast wind speed. The seasonal autoregressive integrated moving average (SARIMA) method and the least squares support vector regression for time series with genetic algorithms (LSSVRTSGA) are used to forecast wind speed in a time series, and the least squares support vector regression with genetic algorithms (LSSVRGA) and DBNGA models are used to predict wind speed in a multivariate format. The empirical results show that forecasting wind speed by the DBNGA models outperforms the other forecasting models in terms of forecasting accuracy. Thus, the DBNGA model is a feasible and effective approach for wind speed forecasting.