{"title":"基于多源数据特征的变电站母线短期负荷预测算法","authors":"Quan Yuan, Qiang Zhang, A. Zhou","doi":"10.1109/AIID51893.2021.9456547","DOIUrl":null,"url":null,"abstract":"In order to avoid the adverse effects of load transfer, power outage and small power supply on bus load forecasting in bus power supply area, a short-term load forecasting algorithm for substation bus based on multi-source data characteristics is proposed. By converting the load of the bus to the ideal power load in the power supply area of the bus, the ideal power load is corrected as the historical load data, and the algorithm of multi-source data characteristic load forecasting is used to obtain the preliminary forecasting results. At the same time, the values of various influencing factors on the day to be forecasted are obtained. The forecasting results eliminate various influencing factors and indirectly predict the load value of the bus. Based on this, the experiment proves that the application of short-term load forecasting algorithm of substation bus based on multi-source data characteristics can significantly improve the accuracy of bus load forecasting with small power supply in the power supply area, compared with the direct forecasting method which takes the load value of bus network as historical data.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Short term load forecasting algorithm of substation bus based on multi source data characteristics\",\"authors\":\"Quan Yuan, Qiang Zhang, A. Zhou\",\"doi\":\"10.1109/AIID51893.2021.9456547\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to avoid the adverse effects of load transfer, power outage and small power supply on bus load forecasting in bus power supply area, a short-term load forecasting algorithm for substation bus based on multi-source data characteristics is proposed. By converting the load of the bus to the ideal power load in the power supply area of the bus, the ideal power load is corrected as the historical load data, and the algorithm of multi-source data characteristic load forecasting is used to obtain the preliminary forecasting results. At the same time, the values of various influencing factors on the day to be forecasted are obtained. The forecasting results eliminate various influencing factors and indirectly predict the load value of the bus. Based on this, the experiment proves that the application of short-term load forecasting algorithm of substation bus based on multi-source data characteristics can significantly improve the accuracy of bus load forecasting with small power supply in the power supply area, compared with the direct forecasting method which takes the load value of bus network as historical data.\",\"PeriodicalId\":412698,\"journal\":{\"name\":\"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AIID51893.2021.9456547\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIID51893.2021.9456547","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Short term load forecasting algorithm of substation bus based on multi source data characteristics
In order to avoid the adverse effects of load transfer, power outage and small power supply on bus load forecasting in bus power supply area, a short-term load forecasting algorithm for substation bus based on multi-source data characteristics is proposed. By converting the load of the bus to the ideal power load in the power supply area of the bus, the ideal power load is corrected as the historical load data, and the algorithm of multi-source data characteristic load forecasting is used to obtain the preliminary forecasting results. At the same time, the values of various influencing factors on the day to be forecasted are obtained. The forecasting results eliminate various influencing factors and indirectly predict the load value of the bus. Based on this, the experiment proves that the application of short-term load forecasting algorithm of substation bus based on multi-source data characteristics can significantly improve the accuracy of bus load forecasting with small power supply in the power supply area, compared with the direct forecasting method which takes the load value of bus network as historical data.