Probabilistic short-term power load forecasting based on B-SCN
Grid management and power dispatching rely on accurate short-term power load prediction. Different algorithms have been constantly developed and tested to improve forecast precision. However, these forecasts are constrained by a number of uncertain factors, which are caused by dynamic environment, t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722018704 |
| _version_ | 1797950725684199424 |
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| author | Yi Ning Ruixuan Zhao Shoujin Wang Baolong Yuan Yilin Wang Di Zheng |
| author_facet | Yi Ning Ruixuan Zhao Shoujin Wang Baolong Yuan Yilin Wang Di Zheng |
| author_sort | Yi Ning |
| collection | DOAJ |
| description | Grid management and power dispatching rely on accurate short-term power load prediction. Different algorithms have been constantly developed and tested to improve forecast precision. However, these forecasts are constrained by a number of uncertain factors, which are caused by dynamic environment, the nonlinearity and stochasticity of power demand. To obtain more accurate load forecasting value and quantify the uncertainty effectively, this research proposes a boosting stochastic configuration network(B-SCN) based probabilistic forecasting method. First, correlation analysis is taken in multidimensional input parameters. Second, an adaptive B-SCN network architecture is proposed to construct the prediction model and improve the stability of model outputs significantly. The probabilistic forecasting is then used to actualize the model’s uncertainty evaluation by creating the confidence intervals using the Gaussian process. Consequently, experimental results reveal that the proposed boosting-SCN prediction model achieves superior forecasting accuracy than the single SCN model and other commonly used forecasting models. The probabilistic forecasting can efficiently obtain the uncertainties in power load data and provide support for system operation. |
| first_indexed | 2024-04-10T22:19:41Z |
| format | Article |
| id | doaj.art-4e990f73dc484db5887b5b5febe3c018 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:19:41Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-4e990f73dc484db5887b5b5febe3c0182023-01-18T04:31:37ZengElsevierEnergy Reports2352-48472022-11-018646655Probabilistic short-term power load forecasting based on B-SCNYi Ning0Ruixuan Zhao1Shoujin Wang2Baolong Yuan3Yilin Wang4Di Zheng5Shenyang jianzhu University, Shenyang 110168, China; Corresponding author.University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Software, Chinese Academy of Sciences, Beijing, 100190, ChinaShenyang jianzhu University, Shenyang 110168, ChinaShenyang jianzhu University, Shenyang 110168, ChinaThe Fourth Construction Engineering Co., Ltd., China Construction Second Engineering Bureau, Tianjin 300457, ChinaShenyang jianzhu University, Shenyang 110168, ChinaGrid management and power dispatching rely on accurate short-term power load prediction. Different algorithms have been constantly developed and tested to improve forecast precision. However, these forecasts are constrained by a number of uncertain factors, which are caused by dynamic environment, the nonlinearity and stochasticity of power demand. To obtain more accurate load forecasting value and quantify the uncertainty effectively, this research proposes a boosting stochastic configuration network(B-SCN) based probabilistic forecasting method. First, correlation analysis is taken in multidimensional input parameters. Second, an adaptive B-SCN network architecture is proposed to construct the prediction model and improve the stability of model outputs significantly. The probabilistic forecasting is then used to actualize the model’s uncertainty evaluation by creating the confidence intervals using the Gaussian process. Consequently, experimental results reveal that the proposed boosting-SCN prediction model achieves superior forecasting accuracy than the single SCN model and other commonly used forecasting models. The probabilistic forecasting can efficiently obtain the uncertainties in power load data and provide support for system operation.http://www.sciencedirect.com/science/article/pii/S2352484722018704Load forecastingStochastic configuration networkProbabilistic forecasting |
| spellingShingle | Yi Ning Ruixuan Zhao Shoujin Wang Baolong Yuan Yilin Wang Di Zheng Probabilistic short-term power load forecasting based on B-SCN Energy Reports Load forecasting Stochastic configuration network Probabilistic forecasting |
| title | Probabilistic short-term power load forecasting based on B-SCN |
| title_full | Probabilistic short-term power load forecasting based on B-SCN |
| title_fullStr | Probabilistic short-term power load forecasting based on B-SCN |
| title_full_unstemmed | Probabilistic short-term power load forecasting based on B-SCN |
| title_short | Probabilistic short-term power load forecasting based on B-SCN |
| title_sort | probabilistic short term power load forecasting based on b scn |
| topic | Load forecasting Stochastic configuration network Probabilistic forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722018704 |
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