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https://hdl.handle.net/20.500.14279/13364| Τίτλος: | A Neural Network Approach for short-term forecasting of PV Generation in Dwellings | Συγγραφείς: | Georgiou, Giorgos S. Christodoulides, Paul Kalogirou, Soteris A. |
Major Field of Science: | Engineering and Technology | Field Category: | Environmental Engineering | Λέξεις-κλειδιά: | Artificial Neural Networks;Buildings;Forecasting;Photovoltaics;Renewable Energy Generation | Ημερομηνία Έκδοσης: | Σεπ-2018 | Πηγή: | 53rd International Universities Power Engineering Conference, 2018, 4-7 September, Glasgow, UK | Conference: | International Universities Power Engineering Conference | Περίληψη: | The use of renewable energy, especially in buildings, has continuously significantly been increasing, due to the need of reducing the demand from energy grids. Hence, forecasting the renewable energy generation is beneficial, even in buildings, as the energy demand can be optimized for a certain time horizon, resulting in the further reduction of the energy bills, as compared to traditional ways such as Net-Metering. This paper shows the preliminary results of an undergoing research regarding the 24-hour prediction of a PV production, in a dwelling in Cyprus. For the given data and the case studied, the results exhibit an overall correlation of 99% approx. and a Mean Squared Error of 1.4% approx. for a cloudy day. | URI: | https://hdl.handle.net/20.500.14279/13364 | DOI: | 10.1109/UPEC.2018.8541925 | Rights: | © 2018 IEEE. | Type: | Conference Papers | Affiliation: | Cyprus University of Technology | Publication Type: | Peer Reviewed |
| Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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