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https://hdl.handle.net/20.500.14279/19086
Τίτλος: | Energy forecasting using multiheaded convolutional neural networks in efficient renewable energy resources equipped with energy storage system | Συγγραφείς: | Aurangzeb, Khursheed Aslam, Sheraz Haider, Syed Irtaza Mohsin, Syed Muhammad Islam, Saif Ul Khattak, Hasan Ali Shah, Sajid |
Major Field of Science: | Engineering and Technology | Field Category: | Environmental Engineering | Λέξεις-κλειδιά: | Renewable energy resources;Energy bills;Integrating energy storage system;Convolutional neural network;Energy storage system | Ημερομηνία Έκδοσης: | 2019 | Πηγή: | Transactions on Emerging Telecommunications Technologies, 2019 | Περιοδικό: | Transactions on Emerging Telecommunications Technologies | Περίληψη: | Renewable energy resources (RERs) motivate electricity users to reduce their energy bills by taking benefit of self-generated green energy. Different studies have already pointed out that, because of the absence of proper technical support and awareness, the energy users were not able to sufficiently take paybacks from the RERs. However, with the commencement of smart grids, the potential benefits of RERs and dynamic pricing schemes can be exploited. Nonetheless, the big issue is the accurate prediction of energy produced by intermittent RERs. In this work, we have proposed an efficient framework by integrating energy storage system (ESS) and RERs with smart homes. This framework has shown significant results, which make it helpful and suitable for energy management at a community level. We applied a multiheaded convolutional neural network model for precise and accurate prediction of produced energy by RERs. Moreover, we have considered a smart community consisting of 80 homes. Simulation results prove that the proposed framework helps to decrease the energy bill of consumers by 58.32% and 63.02% through integration of RERs without and with ESS, respectively. | URI: | https://hdl.handle.net/20.500.14279/19086 | ISSN: | 21613915 | DOI: | 10.1002/ett.3837 | Rights: | © Wiley Attribution-NonCommercial-NoDerivatives 4.0 International |
Type: | Article | Affiliation: | King Saud University Cyprus University of Technology COMSATS University Islamabad Dr. A. Q. Khan Institute of Computer Science and Information Technology |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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