M. Kursa, Sławomir Walkowiak, Lukasz Ligowski, W. Rudnicki
{"title":"利用数值天气预报进行热电联产电厂发电规划","authors":"M. Kursa, Sławomir Walkowiak, Lukasz Ligowski, W. Rudnicki","doi":"10.1109/ISAP.2011.6082211","DOIUrl":null,"url":null,"abstract":"Production of heat and electricity in the cogeneration plant depends on weather, thus forecasting production is dependent on weather forecasts. Here we present the models of the heat production based on two weather forecast models, COAMPS and UM. The linear models that are based on the predicted air temperature can explain up to 90% of variability of production and deteriorate slowly with the range of forecast. The models of heat productions that are based on UM weather forecasts significantly outperforms those that are based on the models based on the COAMPS weather forecasts. The machine learning algorithm random forest is used to improve the basic models. To this end the residuals from the linear models are predicted using various meteorological variables along with variables governing activity of city inhabitants, such as hour of the day or day of the week. This machine learning approach leads to small but significant improvement in comparison to the original model.","PeriodicalId":424662,"journal":{"name":"2011 16th International Conference on Intelligent System Applications to Power Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilising numerical weather forecast for planning electricity production in cogeneration plant\",\"authors\":\"M. Kursa, Sławomir Walkowiak, Lukasz Ligowski, W. Rudnicki\",\"doi\":\"10.1109/ISAP.2011.6082211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Production of heat and electricity in the cogeneration plant depends on weather, thus forecasting production is dependent on weather forecasts. Here we present the models of the heat production based on two weather forecast models, COAMPS and UM. The linear models that are based on the predicted air temperature can explain up to 90% of variability of production and deteriorate slowly with the range of forecast. The models of heat productions that are based on UM weather forecasts significantly outperforms those that are based on the models based on the COAMPS weather forecasts. The machine learning algorithm random forest is used to improve the basic models. To this end the residuals from the linear models are predicted using various meteorological variables along with variables governing activity of city inhabitants, such as hour of the day or day of the week. This machine learning approach leads to small but significant improvement in comparison to the original model.\",\"PeriodicalId\":424662,\"journal\":{\"name\":\"2011 16th International Conference on Intelligent System Applications to Power Systems\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 16th International Conference on Intelligent System Applications to Power Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISAP.2011.6082211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 16th International Conference on Intelligent System Applications to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISAP.2011.6082211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Utilising numerical weather forecast for planning electricity production in cogeneration plant
Production of heat and electricity in the cogeneration plant depends on weather, thus forecasting production is dependent on weather forecasts. Here we present the models of the heat production based on two weather forecast models, COAMPS and UM. The linear models that are based on the predicted air temperature can explain up to 90% of variability of production and deteriorate slowly with the range of forecast. The models of heat productions that are based on UM weather forecasts significantly outperforms those that are based on the models based on the COAMPS weather forecasts. The machine learning algorithm random forest is used to improve the basic models. To this end the residuals from the linear models are predicted using various meteorological variables along with variables governing activity of city inhabitants, such as hour of the day or day of the week. This machine learning approach leads to small but significant improvement in comparison to the original model.