A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants
Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed....
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| Published in: | Energy Vol. 223; p. 120026 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Elsevier Ltd
15.05.2021
Elsevier BV |
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| ISSN: | 0360-5442, 1873-6785 |
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| Abstract | Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed. In this paper, we present a combined method for day-ahead SPG prediction of multi-region photovoltaic (PV) plants. First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA) is implemented which processes the candidate features by assigning appropriate operators. To achieve more accurate forecast results as well as mine the interpretable relationship between SPG and related weather or PV system factors, the SPG physical model is taken into account. The method performance is evaluated by the real SPG data along with meteorological variables of multi-region PV plants in Hokkaido from 2016 to 2018. Results indicate that the combined method provides acceptable accuracy and outperforms several baselines and other methods used for comparison.
•AML is introduced to establish the optimal combination of different forecast models.•The raw data of multi-region PV plants are reconstructed through suitable operators.•SPG mechanism model is applied to generate suitable candidate operators.•GA algorithm is developed to identify optimal operator set for different PV plant. |
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| AbstractList | Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed. In this paper, we present a combined method for day-ahead SPG prediction of multi-region photovoltaic (PV) plants. First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA) is implemented which processes the candidate features by assigning appropriate operators. To achieve more accurate forecast results as well as mine the interpretable relationship between SPG and related weather or PV system factors, the SPG physical model is taken into account. The method performance is evaluated by the real SPG data along with meteorological variables of multi-region PV plants in Hokkaido from 2016 to 2018. Results indicate that the combined method provides acceptable accuracy and outperforms several baselines and other methods used for comparison. Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed. In this paper, we present a combined method for day-ahead SPG prediction of multi-region photovoltaic (PV) plants. First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA) is implemented which processes the candidate features by assigning appropriate operators. To achieve more accurate forecast results as well as mine the interpretable relationship between SPG and related weather or PV system factors, the SPG physical model is taken into account. The method performance is evaluated by the real SPG data along with meteorological variables of multi-region PV plants in Hokkaido from 2016 to 2018. Results indicate that the combined method provides acceptable accuracy and outperforms several baselines and other methods used for comparison. •AML is introduced to establish the optimal combination of different forecast models.•The raw data of multi-region PV plants are reconstructed through suitable operators.•SPG mechanism model is applied to generate suitable candidate operators.•GA algorithm is developed to identify optimal operator set for different PV plant. |
| ArticleNumber | 120026 |
| Author | Zhao, Wei Zhang, Haoran Huang, Liqiao Dai, Yuanhao Shang, Wenlong Liang, Yongtu Zheng, Jianqin |
| Author_xml | – sequence: 1 givenname: Wei surname: Zhao fullname: Zhao, Wei organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 2 givenname: Haoran surname: Zhang fullname: Zhang, Haoran email: zhang_ronan@csis.u-tokyo.ac.jp organization: Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8568, Japan – sequence: 3 givenname: Jianqin surname: Zheng fullname: Zheng, Jianqin email: 2018214074@student.cup.edu.cn organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 4 givenname: Yuanhao surname: Dai fullname: Dai, Yuanhao organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 5 givenname: Liqiao surname: Huang fullname: Huang, Liqiao organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China – sequence: 6 givenname: Wenlong surname: Shang fullname: Shang, Wenlong organization: Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, 100124, PR China – sequence: 7 givenname: Yongtu surname: Liang fullname: Liang, Yongtu organization: National Engineering Laboratory for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing, 102249, PR China |
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| Keywords | Solar power generation prediction Automatic machine learning Multi-region photovoltaic plants Genetic algorithm |
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| Title | A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants |
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