Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network
In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2020-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/73/e3sconf_acic2020_03002.pdf |
| _version_ | 1819182360732631040 |
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| author | Ma Guozhen Hu Po Wang Yunjia Wang Yongli Cai Chengcong Sun Yaling Zhang Xinya |
| author_facet | Ma Guozhen Hu Po Wang Yunjia Wang Yongli Cai Chengcong Sun Yaling Zhang Xinya |
| author_sort | Ma Guozhen |
| collection | DOAJ |
| description | In order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method. |
| first_indexed | 2024-12-22T22:44:54Z |
| format | Article |
| id | doaj.art-2d6a7961af074fa084c94de38fa99d87 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-22T22:44:54Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-2d6a7961af074fa084c94de38fa99d872022-12-21T18:10:06ZengEDP SciencesE3S Web of Conferences2267-12422020-01-012130300210.1051/e3sconf/202021303002e3sconf_acic2020_03002Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural NetworkMa Guozhen0Hu Po1Wang Yunjia2Wang Yongli3Cai Chengcong4Sun Yaling5Zhang Xinya6Economic and Technological Research Institute of State Grid Hebei Electric Power Co., LtdEconomic and Technological Research Institute of State Grid Hebei Electric Power Co., LtdEconomic and Technological Research Institute of State Grid Hebei Electric Power Co., LtdNorth China Electric Power UniversityNorth China Electric Power UniversityNorth China Electric Power UniversityNorth China Electric Power UniversityIn order to solve the diversification of the load characteristics of the distribution network due to the difference in the electric structure and the electricity consumption habits of users, the calculation accuracy of the forecast model is difficult to meet the actual demand. In this paper, through in-depth study of the characteristics of ultra-short-term load, an ultra-short-term load forecasting model based on fuzzy clustering and RBF neural network (FCM-RBF) is constructed. The model not only considers the historical load characteristics of locally similar days, but also considers the current load characteristics of the forecast days. The load on a locally similar day can well reflect the overall trend of the predicted load; the current load on the forecast day can well reflect the changing law of real-time data during the forecast period and some random factors in the forecast period. Finally, a power grid load in a certain area of southwestern China is selected as an example to verify the effectiveness and accuracy of the proposed method.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/73/e3sconf_acic2020_03002.pdf |
| spellingShingle | Ma Guozhen Hu Po Wang Yunjia Wang Yongli Cai Chengcong Sun Yaling Zhang Xinya Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network E3S Web of Conferences |
| title | Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network |
| title_full | Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network |
| title_fullStr | Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network |
| title_full_unstemmed | Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network |
| title_short | Research on Ultra-Short-Term Load Forecasting of Distribution Network Based on Fuzzy Clustering and RBF Neural Network |
| title_sort | research on ultra short term load forecasting of distribution network based on fuzzy clustering and rbf neural network |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/73/e3sconf_acic2020_03002.pdf |
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