Abstract—In this paper, a procedure to analyze customers’ choice of renewable energy technologies (RETs) using artificial neural networks is proposed. The relationships between input data such as the investment phases, grant amounts, date received, property age and total installation corresponding to each consumer and the target data consisting of RETs such as solar, biomass and heat pump are explored and each percentage RET choice are estimated for all phases together and for each phase by using several neural network models developed in this paper. Case studies of domestic dwelling heating in Ireland with the recently published data are analyzed. Through the proposed procedure and the case studies, the following applications are proposed (i) validation of the implemented Irish governmental Greener Homes Scheme and related customers subsidiary policies being enforced, (ii) forecasting customers choices in the future renewable energy schemes if the data on grant, time, property characteristics, expected installation and policy are given, and (iii) transferring and deployment of technologies to developing and emerging economies.
Index Terms—Artificial neural network, customers’ choice, heating, renewable energy technology.
V.-N. Dinh and B. Basu are with Department of Civil, Structural and Environmental Engineering, School of Engineering, Trinity College Dublin, Dublin 2, Ireland (e-mail: nguyendv@tcd.ie, nguyendhhh@yahoo.com, basub@tcd.ie).
M. Kennedy is with Sustainable Energy Authority of Ireland, Wilton Park House, Dublin, Ireland (email: matthew.kennedy@seai.ie).
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Cite:Van-Nguyen Dinh, Biswajit Basu, and Matthew Kennedy, "Development of a Procedure to Analyze Customers’ Choice of Renewable Energy Heating Technologies: Application in Ireland," Journal of Clean Energy Technologies vol. 3, no. 4, pp. 312-316, 2015.
