Using artificial neural networks for temporal and spatial wind speed forecasting in Iran. (1st May 2016)

• Record Type: Journal Article
• Authors: Noorollahi, Younes; Jokar, Mohammad Ali; Kalhor, Ahmad
• Is Part Of: Energy conversion and management. Volume 115(2016)
• Page Start: 17
• Page End: 25
• Publication Date: 2016-05-01
• Subjects: Short-term wind speed prediction -- Location optimization of wind farms -- BPNN -- RBFNN -- ANFIS; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2016.02.041
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: The prediction of wind speed in both temporal and spatial dimensions is investigated. Wind speed characteristics of an arbitrary site can be estimated using data from nearby stations. This type of spatial prediction is the first study in this field worldwide. Abstract: Over the past few years, significant progress has been made in wind power generation worldwide. Because of the turbulent nature of wind velocity, the management of wind intermittence is a substantial field of research in the wind energy sector. This paper presents an investigation of this problem in two parts, the prediction of wind speed in both temporal and spatial dimensions, using artificial neural networks (ANNs). ANNs are novel methods applicable in modeling of complicated systems such as wind speed which generally investigated by a large amount of registered data exemplifying the behavior of. We first predicted the temporal dimension of wind speed at one-hour time interval, as a short-term wind speed prediction, in three wind observation stations (WOSs) in Iran. In the next part, estimation of wind speed data in a WOS using data from some other nearby WOSs was carried out. Due to the limitation of data collection, two groups of WOSs were selected for this target. The average value of the wind speed histogram error obtained from the best model in both groups is about 2.6% which is certainly promising. In Iran, the scarcity of meteorological data has resulted in the limited study of wind energy resources. Therefore, this type of spatial prediction is very useful in wind resource assessment in the Iranian wind energy industry. This is a valuable tool that enables the decision maker to precisely detect the high wind speed areas over an entire region in the first step of investigation.