Evgueniy Entchev Sergio Sibilio , Libing Yang, Mohamed Ghorab, Antonio Rosato
Hybrid microgeneration system; Ground source heat pump; Photovoltaic thermal; Artiﬁcial neural network; Predictive control; Energy saving
Alexandria Engineering Journal
Abstract The use of artiﬁcial neural networks (ANNs) in various applications has grown signiﬁcantly over the years. This paper compares an ANN based approach with a conventional on-off control applied to the operation of a ground source heat pump/photovoltaic thermal system serving a single house located in Ottawa (Canada) for heating and cooling purposes. The hybrid renewable microgeneration system was investigated using the dynamic simulation software TRNSYS. A controller for predicting the future room temperature was developed in the MATLAB environment and six ANN control logics were analyzed The comparison was performed in terms of ability to maintain the desired indoor comfort level sprimary energy consumption, operating costs and carbon dioxide equivalent emissions during a week of the heating period and a week of the cooling period. The results showed that the AN approach is potentially able to alleviate the intensity of thermal discomfort associated with overheating/over cool in phenomena, but it could cause an increase in unmet comfort hours. The analysis(up to around 36%), the operating costs (up to around 81%) as well as the carbon dioxide equivalent(up to around 36%), the operating costs (up to around 81%) as well as the carbon dioxide equivalent emissions (up to around 36%).
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