A hybrid prediction model for photovoltaic power generation based on information entropy
Abstract Photovoltaic power is affected by various random and coupled meteorological factors, and its changing trend implies the non‐linear effects of these factors. According to the quantitative analysis results, a statistical prediction model is proposed to accurately predict the power, which is o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-02-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12032 |
| _version_ | 1828176350324719616 |
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| author | Shiping Pu Zhiyong Li Hui Wan Yougen Chen |
| author_facet | Shiping Pu Zhiyong Li Hui Wan Yougen Chen |
| author_sort | Shiping Pu |
| collection | DOAJ |
| description | Abstract Photovoltaic power is affected by various random and coupled meteorological factors, and its changing trend implies the non‐linear effects of these factors. According to the quantitative analysis results, a statistical prediction model is proposed to accurately predict the power, which is of great significance to the safe and efficient use of solar energy. In this study, the authors first use grey relation analysis to select four main meteorological factors affecting photovoltaic power. Further, they combine grey relation analysis with information entropy and apply grey relation entropy to similar day analysis. On this basis, they take grey relation analysis to optimise extreme learning machine model to establish the grey relation analysis‐extreme learning machine model, while taking similar day analysis to optimise firefly algorithm to establish the similar day analysis‐firefly algorithm. By combining the two sub‐models with information entropy, a hybrid prediction model for photovoltaic power generation based on information entropy is proposed. The experimental results show that in various weather conditions, the values of mean absolute percentage error, root mean square error and standard deviation of error are 2.8425%, 2.5675 and 2.2642, respectively. Therefore, the proposed hybrid model has superior prediction performance. |
| first_indexed | 2024-04-12T04:43:04Z |
| format | Article |
| id | doaj.art-0ce841b5a0344aa383d06d2a32b65fba |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-12T04:43:04Z |
| publishDate | 2021-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-0ce841b5a0344aa383d06d2a32b65fba2022-12-22T03:47:34ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952021-02-0115343645510.1049/gtd2.12032A hybrid prediction model for photovoltaic power generation based on information entropyShiping Pu0Zhiyong Li1Hui Wan2Yougen Chen3School of Automation Central South University Changsha ChinaSchool of Automation Central South University Changsha ChinaSchool of Automation Central South University Changsha ChinaSchool of Automation Central South University Changsha ChinaAbstract Photovoltaic power is affected by various random and coupled meteorological factors, and its changing trend implies the non‐linear effects of these factors. According to the quantitative analysis results, a statistical prediction model is proposed to accurately predict the power, which is of great significance to the safe and efficient use of solar energy. In this study, the authors first use grey relation analysis to select four main meteorological factors affecting photovoltaic power. Further, they combine grey relation analysis with information entropy and apply grey relation entropy to similar day analysis. On this basis, they take grey relation analysis to optimise extreme learning machine model to establish the grey relation analysis‐extreme learning machine model, while taking similar day analysis to optimise firefly algorithm to establish the similar day analysis‐firefly algorithm. By combining the two sub‐models with information entropy, a hybrid prediction model for photovoltaic power generation based on information entropy is proposed. The experimental results show that in various weather conditions, the values of mean absolute percentage error, root mean square error and standard deviation of error are 2.8425%, 2.5675 and 2.2642, respectively. Therefore, the proposed hybrid model has superior prediction performance.https://doi.org/10.1049/gtd2.12032Solar power stations and photovoltaic power systemsOptimisation techniquesInterpolation and function approximation (numerical analysis)Optimisation techniquesInterpolation and function approximation (numerical analysis)Combinatorial mathematics |
| spellingShingle | Shiping Pu Zhiyong Li Hui Wan Yougen Chen A hybrid prediction model for photovoltaic power generation based on information entropy IET Generation, Transmission & Distribution Solar power stations and photovoltaic power systems Optimisation techniques Interpolation and function approximation (numerical analysis) Optimisation techniques Interpolation and function approximation (numerical analysis) Combinatorial mathematics |
| title | A hybrid prediction model for photovoltaic power generation based on information entropy |
| title_full | A hybrid prediction model for photovoltaic power generation based on information entropy |
| title_fullStr | A hybrid prediction model for photovoltaic power generation based on information entropy |
| title_full_unstemmed | A hybrid prediction model for photovoltaic power generation based on information entropy |
| title_short | A hybrid prediction model for photovoltaic power generation based on information entropy |
| title_sort | hybrid prediction model for photovoltaic power generation based on information entropy |
| topic | Solar power stations and photovoltaic power systems Optimisation techniques Interpolation and function approximation (numerical analysis) Optimisation techniques Interpolation and function approximation (numerical analysis) Combinatorial mathematics |
| url | https://doi.org/10.1049/gtd2.12032 |
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