Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation

Erdong Zhao, Jing Zhao, Liwei Liu, Zhongyue Su and Ning An
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Erdong Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Jing Zhao: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Liwei Liu: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Zhongyue Su: College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Ning An: Gerontechnology Lab, School of Computer and Information, Hefei University of Technology, Hefei 230009, China

Energies, 2015, vol. 9, issue 1, 1-20

Abstract: Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA) auto-regressive integrated moving average with exogenous variables (ARIMAX) model that is optimized very-short-term by the chaotic particle swarm optimization (CPSO) algorithm, known as the SA-ARIMA-CPSO approach, for wind speed prediction. The ARIMAX model chooses the wind speed result from the Weather Research and Forecasting (WRF) simulation as an exogenous input variable. Further, an SA strategy is applied to the ARIMAX process. When new information is available, the model process can be updated adaptively with parameters optimized by the CPSO algorithm. The proposed SA-ARIMA-CPSO approach enables the forecasting process to update training information and model parameters intelligently and adaptively. As tested using the 15-min wind speed data collected from a wind farm in Northern China, the improved method has the best performance compared with several other models.

Keywords: wind speed; self-adaptive strategy; ARIMAX; WRF simulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
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