A machine learning based heating and cooling load forecasting approach for DHC networks

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.

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Prediction of Heating and Cooling Load to improve Energy Efficiency of Buildings Using Machine Learning Techniques

JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES ◽

10.26782/jmcms.2018.12.00008 ◽

2018 ◽

Vol 13 (5) ◽

Author(s):

Srihari J

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

Machine Learning Techniques ◽

Cooling Load ◽

Improve Energy Efficiency ◽

Heating And Cooling ◽

Learning Techniques ◽

Heating And Cooling Load

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A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes

Energy and Buildings ◽

10.1016/j.enbuild.2018.06.056 ◽

2018 ◽

Vol 176 ◽

pp. 275-286 ◽

Cited By ~ 14

Author(s):

Sachin Kumar ◽

Saibal K. Pal ◽

Ram Pal Singh

Keyword(s):

Extreme Learning Machine ◽

Cooling Load ◽

Heating And Cooling ◽

Novel Method ◽

Learning Machine ◽

Heating And Cooling Load

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Impact of building design parameters precision on heating and cooling load calculations

Environmental Progress & Sustainable Energy ◽

10.1002/ep.13106 ◽

2018 ◽

Vol 38 (2) ◽

pp. 741-749

Author(s):

Sajad Abasnezhad ◽

Nima Soltani ◽

Elin Markarian ◽

Hamed Aghabalayi Fakhim ◽

Hamed Khezerloo

Keyword(s):

Building Design ◽

Design Parameters ◽

Cooling Load ◽

Heating And Cooling ◽

Heating And Cooling Load

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The Effects of Window Film on the Performance of Window System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.525.408 ◽

2014 ◽

Vol 525 ◽

pp. 408-411

Author(s):

Min Seon Jang ◽

Gyeong Seok Choi ◽

Jae Sik Kang ◽

Yumin Kim

Keyword(s):

Energy Consumption ◽

Cooling Load ◽

Indoor Temperature ◽

Heating And Cooling ◽

Window System ◽

Window Systems ◽

Clear Glass ◽

Window Film ◽

And Performance ◽

Heating And Cooling Load

Window film is generally attached the glazing in buildings to improve the thermal performance of the window system by addressing a range of problems such as indoor temperature rise, indoor temperature imbalance, degraded heating and cooling load due to excessive influx of solar radiation. To evaluate the performance of window films, window films are attached to 3mm or 6mm clear glass. However, window films are generally used on existing window systems for reducing the annual energy consumption. Therefore it is necessary to evaluate the performance of window films depending on the performance of glazing such as clear double glazing or low-e double glazing. Thus the purpose of this study is to analyze the performance of window systems when window film is attached. As a result, in the case of applying window films for reducing the SHGC of buildings, it is necessary to select window films suitable for the configuration and performance of the glazing to be installed, considering the SHGC of the entire glazing system.

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