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Overview of the use of artificial neural networks for energy‐related applications in the building sector
Author(s) -
Guyot Dimitri,
Giraud Florine,
Simon Florian,
Corgier David,
Marvillet Christophe,
Tremeac Brice
Publication year - 2019
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.4706
Subject(s) - artificial neural network , energy consumption , greenhouse gas , computer science , consumption (sociology) , engineering , architectural engineering , environmental economics , artificial intelligence , risk analysis (engineering) , business , economics , ecology , social science , sociology , electrical engineering , biology
Summary The incessant growing of the world's energy consumption and associated greenhouse gases emissions have created tremendous problems to be solved by today's and future generations. As the building sector is one of the biggest energy consumers, reducing its energy consumption is now mandatory. Being able to conceive and built efficient buildings, to effectively manage and operate them, and to rapidly renovate the existing building stock is a challenging task. Neural networks models open new possibilities to address this problem. This paper offers a comprehensive review of the studies that use neural networks for energy‐related applications in the building sector focusing on their application and on the technical characteristics of the network (ie, learning algorithm, number of layers, number of neurons, inputs and output variables, and performance criteria). On the basis of this review, limitations concerning the use of neural networks in the building sector along with existing research gaps and future research directions are identified.