Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/13369
Title: Implementing artificial neural networks in energy building applications - A review
Authors: Georgiou, Georgios C. 
Christodoulides, Paul 
Kalogirou, Soteris A. 
Keywords: Artificial Intelligence;Artificial Neural Networks;Buildings;Energy;Renewable Energy
Category: Electrical Engineering - Electronic Engineering - Information Engineering;Environmental Engineering
Field: Engineering and Technology
Issue Date: Jun-2018
Source: 5th IEEE International Energy Conference, 2018, 3-7 June, Limassol, Cyprus
Conference: IEEE International Energy Conference 
Abstract: Artificial Neural Networks (ANNs) constitute a research area of high interest, for both practitioners and academics, as they are found very useful for solving complex problems that are difficult to solve using known and well developed conventional methods or techniques. They can be used for prediction, control, estimation, data clustering and many other applications that are found in everyday scenarios. This paper explains in brief the basic theory of ANNs, followed by a review of different studies related to ANNs used for applications in buildings such as energy management, systems control and energy prediction. It has been found that applying ANNs in buildings the energy consumption can be reduced, depending on the application. Furthermore, efficient control mechanisms also become possible, leading to the reduction of the energy consumption. Through this review, the reader will be able to recognise the value of ANNs and their big potential in buildings and energy sector, in general. Finally, an ANN-based structure for predicting the local RES generation and the load demand for a building is proposed.
URI: http://ktisis.cut.ac.cy/handle/10488/13369
ISBN: 978-1-5386-3669-5
DOI: 10.1109/ENERGYCON.2018.8398847
Rights: © 2018 IEEE.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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