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

被引:101
作者
Moradzadeh, Arash [1 ]
Mansour-Saatloo, Amin [1 ]
Mohammadi-Ivatloo, Behnam [1 ,2 ]
Anvari-Moghaddam, Amjad [1 ,3 ]
机构
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz 5166616471, Iran
[2] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[3] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 11期
关键词
energy management; load forecasting; heating and cooling; machine learning; multi-layer perceptron (MLP); support vector regression (SVR); ARTIFICIAL-INTELLIGENCE; THERMAL COMFORT; ENERGY; PREDICTION; REGRESSION; NETWORK; OPERATION;
D O I
10.3390/app10113829
中图分类号
O6 [化学];
学科分类号
0703 ;
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.
引用
收藏
页数:12
相关论文
共 42 条
[11]   Cooling load prediction and optimal operation of HVAC systems using a multiple nonlinear regression model [J].
Fan, Chengliang ;
Ding, Yunfei .
ENERGY AND BUILDINGS, 2019, 197 :7-17
[12]  
Haykin S. S., 2009, NEURAL NETWORKS LEAR
[13]  
Jing ZJ, 2019, INNOV SMART GRID TEC, DOI [10.1109/isgt.2019.8791654, 10.1109/eurosime.2019.8724571]
[14]   Decision Support Application for Energy Consumption Forecasting [J].
Jozi, Aria ;
Pinto, Tiago ;
Praca, Isabel ;
Vale, Zita .
APPLIED SCIENCES-BASEL, 2019, 9 (04)
[15]   Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings [J].
Kneifel, Joshua .
ENERGY AND BUILDINGS, 2010, 42 (03) :333-340
[16]   A study of the importance of occupancy to building cooling load in prediction by intelligent approach [J].
Kwok, Simon S. K. ;
Lee, Eric W. M. .
ENERGY CONVERSION AND MANAGEMENT, 2011, 52 (07) :2555-2564
[17]   A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings' Energy Efficiency for Smart City Planning [J].
Le Thi Le ;
Hoang Nguyen ;
Dou, Jie ;
Zhou, Jian .
APPLIED SCIENCES-BASEL, 2019, 9 (13)
[18]   The role of data sample size and dimensionality in neural network based forecasting of building heating related variables [J].
Macas, Martin ;
Moretti, Fabio ;
Fonti, Alessandro ;
Giantomassi, Andrea ;
Comodi, Gabriele ;
Annunziato, Mauro ;
Pizzuti, Stefano ;
Capra, Alfredo .
ENERGY AND BUILDINGS, 2016, 111 :299-310
[19]   Robust scheduling of hydrogen based smart micro energy hub with integrated demand response [J].
Mansour-Saatloo, Amin ;
Agabalaye-Rahvar, Masoud ;
Mirzaei, Mohammad Amin ;
Mohammadi-Ivatloo, Behnam ;
Abapour, Mehdi ;
Zare, Kazem .
JOURNAL OF CLEANER PRODUCTION, 2020, 267
[20]   Stochastic security-constrained operation of wind and hydrogen energy storage systems integrated with price-based demand response [J].
Mirzaei, Mohammad Amin ;
Yazdankhah, Ahmad Sadeghi ;
Mohammadi-Ivatloo, Behnam .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2019, 44 (27) :14217-14227