Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

Wei Dong, Qiang Yang and Xinli Fang
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Wei Dong: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Qiang Yang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Xinli Fang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2018, vol. 11, issue 8, 1-19

Abstract: Accurate generation prediction at multiple time-steps is of paramount importance for reliable and economical operation of wind farms. This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS). The PSR technique transforms the historical time series into a set of phase-space variables combining with the numerical weather prediction (NWP) data to prepare candidate inputs. A minimal redundancy maximal relevance (mRMR) criterion based filtering approach is used to automatically select the optimal input variables for the multi-step ahead prediction. Then, the input instances are divided into a set of subsets using the K-means clustering to train the ANFIS. The ANFIS parameters are further optimized to improve the prediction performance by the use of particle swarm optimization (PSO) algorithm. The proposed solution is extensively evaluated through case studies of two realistic wind farms and the numerical results clearly confirm its effectiveness and improved prediction accuracy compared to benchmark solutions.

Keywords: multi-step ahead prediction; phase space reconstruction; input variable selection; K-means clustering; neuro-fuzzy inference system; wind power prediction (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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