Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

Applied Sciences

卷:10

Behnam Mohammadi-Ivatloo¹  Arash Moradzadeh¹  Amjad Anvari-Moghaddam¹  Amin Mansour-Saatloo¹ 

[1] Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran;

关键词: energy management;    load forecasting;    heating and cooling;    machine learning;    multi-layer perceptron (MLP), support vector regression (SVR);   

DOI  :  10.3390/app10113829

来源: DOAJ

【 摘 要 】

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

【 授权许可】

Unknown