Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

Zhongrong Zhang, Yiliao Song, Feng Liu and Jinpeng Liu
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Zhongrong Zhang: School of Mathematics and Physics, Lanzhou Jiaotong University, No. 88, West Annin Road, Anning District, Lanzhou 730070, China
Yiliao Song: School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Street, Shahekou District, Dalian 116025, China
Feng Liu: School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Street, Shahekou District, Dalian 116025, China
Jinpeng Liu: Geological Natural Disaster Prevention Research Institute, Gansu Academy of Sciences, No. 229, Dinxi South Road, Chengguan District, Lanzhou 730070, China

Sustainability, 2016, vol. 8, issue 2, 1-30

Abstract: Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Singular Spectrum Analysis (SSA), this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm), to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

Keywords: average wind power; interval forecasts; optimal subtractive clustering method; ANFIS; SSA; Model comparison (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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