A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM

Sen Wang, Yonghui Sun, Yan Zhou, Rabea Jamil Mahfoud and Dongchen Hou
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Sen Wang: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Yonghui Sun: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Yan Zhou: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Rabea Jamil Mahfoud: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Dongchen Hou: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China

Energies, 2019, vol. 13, issue 1, 1-17

Abstract: The main characteristics of the photovoltaic (PV) output power are the randomness and uncertainty, such features make it not easy to establish an accurate forecasting method. The accurate short-term forecasting of PV output power has great significance for the stability, safe operation and economic dispatch of the power grid. The deterministic point forecast method ignores the randomness and volatility of PV output power. Aiming at overcoming those defects, this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition (EEMD) as well as relevance vector machine (RVM). Firstly, the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions (IMFs) and residual (RES) components. After that, based on the decomposed components, the sample entropy (SE) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained. Then, by implementing RVM, the forecasting model for every component is developed. Finally, the forecasting results of every new component are superimposed in order to achieve the overall forecasting results with certain confidence level. Simulation results demonstrate, by comparing them with some previous methods, that the hybrid method based on EEMD-SE-RVM has relatively higher forecasting accuracy, more reliable forecasting interval and high engineering application value.

Keywords: photovoltaic output power forecasting; hybrid interval forecasting; relevance vector machine; sample entropy; ensemble empirical mode decomposition (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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