{"title":"PJM电力市场及upppcl提前一小时负荷预测","authors":"Kishan Bhushan Sahay, Vishesh Rana","doi":"10.1109/ENERGYECONOMICS.2015.7235068","DOIUrl":null,"url":null,"abstract":"Short-term load forecasting is an essential instrument in power system planning, operation & control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis & maintenance planning for the generators. This paper discusses significant role of artificial intelligence (AI) in short-term load forecasting (STLF), that is, the one hour-ahead forecast of the power system load. A new artificial neural network (ANN) has been designed to compute the forecasted load. Historical electricity load data has been used in the modeling of ANN. The ANN model is trained on hourly data from PJM Electricity Market & UPPCL and tested on out-of-sample data. Simulation results obtained have shown that one hour-ahead forecasts of load using proposed ANN is very accurate with very less error.","PeriodicalId":130355,"journal":{"name":"2015 International Conference on Energy Economics and Environment (ICEEE)","volume":"340 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"One hour ahead load forecast of PJM electricity market & UPPCL\",\"authors\":\"Kishan Bhushan Sahay, Vishesh Rana\",\"doi\":\"10.1109/ENERGYECONOMICS.2015.7235068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Short-term load forecasting is an essential instrument in power system planning, operation & control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis & maintenance planning for the generators. This paper discusses significant role of artificial intelligence (AI) in short-term load forecasting (STLF), that is, the one hour-ahead forecast of the power system load. A new artificial neural network (ANN) has been designed to compute the forecasted load. Historical electricity load data has been used in the modeling of ANN. The ANN model is trained on hourly data from PJM Electricity Market & UPPCL and tested on out-of-sample data. Simulation results obtained have shown that one hour-ahead forecasts of load using proposed ANN is very accurate with very less error.\",\"PeriodicalId\":130355,\"journal\":{\"name\":\"2015 International Conference on Energy Economics and Environment (ICEEE)\",\"volume\":\"340 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Energy Economics and Environment (ICEEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ENERGYECONOMICS.2015.7235068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Energy Economics and Environment (ICEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ENERGYECONOMICS.2015.7235068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
One hour ahead load forecast of PJM electricity market & UPPCL
Short-term load forecasting is an essential instrument in power system planning, operation & control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis & maintenance planning for the generators. This paper discusses significant role of artificial intelligence (AI) in short-term load forecasting (STLF), that is, the one hour-ahead forecast of the power system load. A new artificial neural network (ANN) has been designed to compute the forecasted load. Historical electricity load data has been used in the modeling of ANN. The ANN model is trained on hourly data from PJM Electricity Market & UPPCL and tested on out-of-sample data. Simulation results obtained have shown that one hour-ahead forecasts of load using proposed ANN is very accurate with very less error.