A new strategy for predicting short-term wind speed using soft computing models

• Published in: Renewable & sustainable energy reviews Vol. 16; no. 7; pp. 4563 - 4573
• Main Authors: Haque, Ashraf U., Mandal, Paras, Kaye, Mary E., Meng, Julian, Chang, Liuchen, Senjyu, Tomonobu
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.09.2012; Elsevier
• Subjects: Adaptive neuro-fuzzy inference system; Applied sciences; Artificial neural networks; Back propagation; Backpropagation neural network; Energy; Exact sciences and technology; Forecasting; fuzzy logic; generators (equipment); Mathematical models; meteorological data; Neural networks; power generation; prediction; Radial basis function neural network; Short-term wind speed forecasting; Similar days; Soft computing; Wind power; Wind speed; Short-term wind speed forecasting; Backpropagation neural network; Adaptive neuro-fuzzy inference system; Radial basis function neural network; Similar days
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2012.05.042

Wind power prediction is a widely used tool for the large-scale integration of intermittent wind-powered generators into power systems. Given the cubic relationship between wind speed and wind power, accurate forecasting of wind speed is imperative for the estimation of future wind power generation output. This paper presents a performance analysis of short-term wind speed prediction techniques based on soft computing models (SCMs) formulated on a backpropagation neural network (BPNN), a radial basis function neural network (RBFNN), and an adaptive neuro-fuzzy inference system (ANFIS). The forecasting performance of the SCMs is augmented by a similar days (SD) method, which considers similar historical weather information corresponding to the forecasting day in order to determine similar wind speed days for processing. The test results demonstrate that all evaluated SCMs incur some level of performance improvement with the addition of SD pre-processing. As an example, the SD+ANFIS model can provide up to 48% improvement in forecasting accuracy when compared to the individual ANFIS model alone.