{"title":"利用历史天气数据和气候变化预测进行负荷消耗预测","authors":"Po-Chen Chen, M. Kezunovic","doi":"10.1109/ISAP.2017.8071415","DOIUrl":null,"url":null,"abstract":"The weather impact a major factor in operation of power systems. From the long-term planning perspective, it is not enough to predict whether impacts caused by short-term changes in the atmosphere but one also needs to account for the impact of long-term climate change as well. This paper demonstrates how to utilize the historical weather data and climate change projections in a large (macro) geographical area to predict future load patterns in a relatively small (micro) geographical area. The results show that the impact of temperature rising can have either positive or negative impact on the load, and the deviations may be large depending on the projected climate change data.","PeriodicalId":257100,"journal":{"name":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Load consumption prediction utilizing historical weather data and climate change projections\",\"authors\":\"Po-Chen Chen, M. Kezunovic\",\"doi\":\"10.1109/ISAP.2017.8071415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The weather impact a major factor in operation of power systems. From the long-term planning perspective, it is not enough to predict whether impacts caused by short-term changes in the atmosphere but one also needs to account for the impact of long-term climate change as well. This paper demonstrates how to utilize the historical weather data and climate change projections in a large (macro) geographical area to predict future load patterns in a relatively small (micro) geographical area. The results show that the impact of temperature rising can have either positive or negative impact on the load, and the deviations may be large depending on the projected climate change data.\",\"PeriodicalId\":257100,\"journal\":{\"name\":\"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISAP.2017.8071415\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 19th International Conference on Intelligent System Application to Power Systems (ISAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISAP.2017.8071415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Load consumption prediction utilizing historical weather data and climate change projections
The weather impact a major factor in operation of power systems. From the long-term planning perspective, it is not enough to predict whether impacts caused by short-term changes in the atmosphere but one also needs to account for the impact of long-term climate change as well. This paper demonstrates how to utilize the historical weather data and climate change projections in a large (macro) geographical area to predict future load patterns in a relatively small (micro) geographical area. The results show that the impact of temperature rising can have either positive or negative impact on the load, and the deviations may be large depending on the projected climate change data.