| Ensemble Machine Learning Method for Photovoltaic Power Forecasting | |
| Q. Zhou; X. Tang; Q. Lv; Z. Li; J. Shen; J. Wang; B. Yong | |
| 2023-06-22 | |
| Source Publication | 2023 26th International Conference on Computer Supported Cooperative Work in Design (CSCWD) Impact Factor & Quartile |
| ISSN | 2768-1904 |
| Pages | 332-337 |
| Abstract | Photovoltaic power prediction plays an extremely important role in the construction of smart power grid and power grid security protection. In order to solve the problem of unstable power generation and even damages to the power grid caused by the ever changing irradiance and meteorological conditions, this paper leverages the traditional time series prediction modeling method to the machine learning approaches, such as recurrent neural network (RNN), convolutional neural network (CNN) and decision tree (DT), and uses the ensemble machine learning method to improve the final prediction accuracy, by training and testing on the radiation and meteorological data collected from a photovoltaic power station in Gansu Province of China, which enjoys the best solar resources in the country. The experimental results show that the ensemble model achieves the highest prediction accuracy, and its root mean square error(RMSE) is 0.4477. This is of great significance to the power generation evaluation and dispatching of photovoltaic power station. |
| Keyword | Photovoltaic power prediction Smart grid XG-Boost |
| Publisher | IEEE |
| DOI | 10.1109/CSCWD57460.2023.10152827 |
| Indexed By | IEEE |
| Language | 英语 |
| Funding Organization | IEEE Systems, Man, and Cybernetics Society (SMC); Kunming University; University of Tech of Compiegne (UTC); Yunnan University; Zhejiang University Institute of Computing Innovation |
| EI Accession Number | 20232914401979 |
| EI Keywords | Recurrent neural networks |
| EI Classification Number | 615.2 Solar Power706.1 Electric Power Systems706.1.1 Electric Power Transmission921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory922.2 Mathematical Statistics961 Systems Science |
| Original Document Type | Conference article (CA) |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/529104 |
| Collection | 兰州大学 信息科学与工程学院 |
| Affiliation | 1.School of Information Science and Engineering, Lanzhou University, Lanzhou, China 2.School of Information Science and Engineering, Lanzhou University, Lanzhou, China 3.School of Information Science and Engineering, Lanzhou University, Lanzhou, China 4.School of Information Science and Engineering, Lanzhou University, Lanzhou, China 5.School of Computing and Information Technology, University of Wollongong, Wollongong, Australia 6.School of Information Science and Engineering, Lanzhou University, Lanzhou, China 7.School of Information Science and Engineering, Lanzhou University, Lanzhou, China |
| Recommended Citation GB/T 7714 | Q. Zhou,X. Tang,Q. Lv,et al. Ensemble Machine Learning Method for Photovoltaic Power Forecasting[C],2023:332-337. |
| Files in This Item: | There are no files associated with this item. | ||||
