期刊
ENERGY
卷 217, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.119361
关键词
Artificial intelligence; Combined forecasting system; Data preprocessing; Sub-model selection strategy; Wind speed forecasting
资金
- National Natural Science Foundation of China [71573034]
Wind speed forecasting is becoming increasingly important as the use of wind energy in electricity systems grows. A combined prediction system has been proposed in this study, integrating multiple forecasting methods to provide accurate point and interval forecasts for wind speed. This system is deemed more useful for the scheduling and management of electric power systems compared to other benchmark models.
Wind speed forecasting is gaining importance as the share of wind energy in electricity systems increases. Numerous forecasting approaches have been used to predict wind speeds. However, considering the differences in wind speed time-series, there is no universal approach that has proven to be accurate under all circumstances. In our study, a combined prediction system is proposed, which consists of four parts: optimal sub-model selection, point prediction based on a modified multi-objective optimization algorithm, interval forecasting based on distribution fitting, and forecasting system evaluation. The developed combined system integrates the merits of the sub-models and provides accurate point and interval forecasting performance. The experimental results reveal that the proposed combined forecasting system can provide effective wind speed point and interval forecasts. The absolute percentage error values of the proposed system for point forecasting are 2.9220%, 3.1696%, and 4.8358% at Site 1 and 2.2719%, 2.5882%, and 3.4799% at Site 2 for one-, two-, and three-step forecasts, respectively. Therefore, the proposed system is deemed more useful for the scheduling and management of electric power systems than other benchmark models. (C) 2020 Elsevier Ltd. All rights reserved.
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