Reduced LS-SVM을 이용한 지역난방 동절기 공동주택 난방부하의 모델링

Abstract

A model of apartment heating load in a district heating system could be useful in the management and utilization of energy resources, since it could predict energy usage and so could assist in the efficient use of energy resources. The heating load in a district heating system varies in a highly nonlinear manner and is subject to many different factors, such as heating area, number of people living in that complex, and ambient temperature. Thus there are few published papers with accurate models of heating load, especially in domestic literature. This work is concerned with the modeling of apartment heating load in a district heating system in winter, using the reduced least square support vector machine (LS-SVM), and with the purpose of using the model to predict heating energy usage in domestic city area. We collected 23,856 pieces of data on heating energy usage over a 12-week period in winter, from 12 heat exchangers in five apartments. Half of the collected data were used to construct the heating load model, and the other half were used to test the model`s accuracy. The model was able to predict the heating energy usage pattern rather accurately. It could also estimate the usage of heating energy within of mean absolute percentage error. This implies that the model prediction accuracy needs to be improved further, but it still could be considered as an acceptable model if we consider the nonlinearity and uncertainty of apartment heating energy usage in a district heating system.