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Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

Hui Wang (), Jianbo Sun () and Weijun Wang ()
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Hui Wang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Jianbo Sun: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Weijun Wang: Department of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China

Sustainability, 2018, vol. 10, issue 8, 1-11

Abstract: It is widely considered that solar energy will be one of the most competitive energy sources in the future, and solar energy currently accounts for high percentages of power generation in developed countries. However, its power generation capacity is significantly affected by several factors; therefore, accurate prediction of solar power generation is necessary. This paper proposes a photovoltaic (PV) power generation forecasting method based on ensemble empirical mode decomposition (EEMD) and variable-weight combination forecasting. First, EEMD is applied to decompose PV power data into components that are then combined into three groups: low-frequency, intermediate-frequency, and high-frequency. These three groups of sequences are individually predicted by the variable-weight combination forecasting model and added to obtain the final forecasting result. In addition, the design of the weights for combination forecasting was studied during the forecasting process. The comparison in the case study indicates that in PV power generation forecasting, the prediction results obtained by the individual forecasting and summing of the sequences after the EEMD are better than those from direct prediction. In addition, when the single prediction model is converted to a variable-weight combination forecasting model, the prediction accuracy is further improved by using the optimal weights.

Keywords: EEMD; variable-weight combination forecasting; harmonic mean; photovoltaic (PV) power generation forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2018
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