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      A STUDY ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR PREDICTING THE HEATING AND COOLING LOADS OF BUILDINGS

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          Abstract

          The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.

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          Most cited references26

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          A review on buildings energy consumption information

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            Is Open Access

            Heating and cooling energy trends and drivers in buildings

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              A short-term building cooling load prediction method using deep learning algorithms

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                Author and article information

                Journal
                jgrb
                Journal of Green Building
                College Publishing
                1552-6100
                1943-4618
                1943-4618
                Summer 2019
                : 14
                : 3
                : 115-128
                Author notes

                1. Department of Electrical Engineering, Trident Academy of Technology, Bhubaneswar, India

                2. School of Computer Engineering, KIIT University, Bhubaneswar, India;

                *Corresponding author: aleena.swetapadma@ 123456kiit.ac.in

                3. School of Electrical Engineering, KIIT University, Bhubaneswar, India

                Article
                jgb.14.3.115
                10.3992/1943-4618.14.3.115
                02e5b8cb-b2f8-41df-bdb8-df517d82c2c6
                © 2019 College Publishing
                History
                Page count
                Pages: 14
                Categories
                RESEARCH

                Urban design & Planning,Civil engineering,Environmental management, Policy & Planning,Architecture,Environmental engineering
                Random Forest,Iteratively Reweighted Least Squares,Support Vector Machine,Artificial Neural Network,heating and cooling load,building energy performance

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