A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period

Yuan An, Kaikai Dang, Xiaoyu Shi, Rong Jia, Kai Zhang and Qiang Huang
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Yuan An: College of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Kaikai Dang: College of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Xiaoyu Shi: College of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Rong Jia: College of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Kai Zhang: College of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Qiang Huang: College of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China

Energies, 2021, vol. 14, issue 4, 1-18

Abstract: Due to the large number of grid connection of distributed power supply, the existing scheduling methods can not meet the demand gradually. The proposed virtual power plant provides a new idea to solve this problem. The photovoltaic power prediction provides the data basis for the scheduling of the virtual power plant. Prediction intervals of photovoltaic power is a powerful statistical tool used for quantifying the uncertainty of photovoltaic power generation in power systems. To improve the interval prediction accuracy during the non-stationary periods of photovoltaic power, this paper proposes a probabilistic ensemble prediction model, which combines the modules of data preprocessing, non-stationary period discrimination, feature extraction, deterministic prediction, uncertainty prediction, and optimization integration into a general framework. More specifically, in the non-stationary period discrimination module, the method of discriminating the difference of the power ratio difference is introduced and applied for identifying the non-stationary period of the data of photovoltaic output; in the deterministic point prediction module, a stacking- long-short-term memory neural network model is used for point forecasts; in the uncertainty interval prediction module, a BAYES neural network is introduced for probabilistic forecasts; in the optimization integration module, an optimization algorithm named Non-dominated Sorting Genetic Algorithm-II is applied for integrating and optimizing the results of the point forecast and probabilistic forecast. The proposed model is tested using two photovoltaic outputs and weather data measured from a grid-connected photovoltaic system. The results show that the proposed model outperforms conventional forecast methods to predict short-term photovoltaic power outputs and associated uncertainties. The interval width is reduced by 10–20%, and the prediction accuracy is improved by at least 10%; this can be a useful tool for photovoltaic power forecasting.

Keywords: multi-objective optimization; photovoltaic power; point prediction; interval prediction; ensemble probability prediction (MLBN) model (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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