{"title":"神经网络与遗传算法在能源市场日前价格预测中的比较","authors":"K. Sarada, V. Bapiraju","doi":"10.1109/ISEG.2014.7005607","DOIUrl":null,"url":null,"abstract":"Price prognostication has become progressively relevant to producers and shoppers within the new competitive wattage markets. Both for spot markets and long term contractors, value forecasts square measure necessary to develop bidding ways. In this paper, Genetic Algorithm based Neural network (GANN) approach is used to forecast short term hourly electricity price and the results are compared with Aritificial Neural Network(ANN). The recommended method is studied on the PJM electricity market. The results achieved through the simulation illustrates that the proposed model offers exact and improved results.","PeriodicalId":105826,"journal":{"name":"2014 International Conference on Smart Electric Grid (ISEG)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Comparison of day-ahead price forecasting in energy market using Neural Network and Genetic Algorithm\",\"authors\":\"K. Sarada, V. Bapiraju\",\"doi\":\"10.1109/ISEG.2014.7005607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Price prognostication has become progressively relevant to producers and shoppers within the new competitive wattage markets. Both for spot markets and long term contractors, value forecasts square measure necessary to develop bidding ways. In this paper, Genetic Algorithm based Neural network (GANN) approach is used to forecast short term hourly electricity price and the results are compared with Aritificial Neural Network(ANN). The recommended method is studied on the PJM electricity market. The results achieved through the simulation illustrates that the proposed model offers exact and improved results.\",\"PeriodicalId\":105826,\"journal\":{\"name\":\"2014 International Conference on Smart Electric Grid (ISEG)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Smart Electric Grid (ISEG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISEG.2014.7005607\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Smart Electric Grid (ISEG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISEG.2014.7005607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison of day-ahead price forecasting in energy market using Neural Network and Genetic Algorithm
Price prognostication has become progressively relevant to producers and shoppers within the new competitive wattage markets. Both for spot markets and long term contractors, value forecasts square measure necessary to develop bidding ways. In this paper, Genetic Algorithm based Neural network (GANN) approach is used to forecast short term hourly electricity price and the results are compared with Aritificial Neural Network(ANN). The recommended method is studied on the PJM electricity market. The results achieved through the simulation illustrates that the proposed model offers exact and improved results.