Journal article
A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period
Energies (Basel), v 14(4), p859
01 Feb 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period
- Creators
- Yuan An - Xian Univ Technol, Coll Elect Engn, Xian 710048, Peoples R ChinaKaikai Dang - Xian Univ Technol, Coll Elect Engn, Xian 710048, Peoples R ChinaXiaoyu Shi - Xian Univ Technol, Coll Elect Engn, Xian 710048, Peoples R ChinaRong Jia - Xian Univ Technol, Coll Elect Engn, Xian 710048, Peoples R ChinaKai Zhang - Xian Univ Technol, Coll Elect Engn, Xian 710048, Peoples R ChinaQiang Huang - Xian Univ Technol, Coll Elect Engn, Xian 710048, Peoples R China
- Publication Details
- Energies (Basel), v 14(4), p859
- Publisher
- Mdpi
- Number of pages
- 18
- Grant note
- 2020M673453 / China Postdoctoral Science Foundation 51879213 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) BX20200276 / National Postdoctoral Program for Innovative Talent of China 2019ZDLGY18-03 / Research on comprehensive energy system of park based on big data analysis technology
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000623442900001
- Scopus ID
- 2-s2.0-85106411212
- Other Identifier
- 991020547793104721
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- Web of Science research areas
- Energy & Fuels