Smart grid data analytics framework for increasing energy savings in residential buildings |
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| Affiliation: | 1. Department of Computer Science, GIFT University, Gujranwala, Pakistan;2. FAST School of Computing, National University of Computer and Emerging Sciences, Lahore, Pakistan;1. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;2. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;3. Department of Civil Engineering, University of Architecture Ho Chi Minh City, Ho Chi Minh City, Viet Nam;4. Department of Industrial Education, The University of Danang-University of Technology and Education, Da Nang, Viet Nam;5. Department of Civil Engineering, The University of Danang-University of Technology and Education, Da Nang, Viet Nam;1. School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran;2. Industrial Engineering Department, Yazd University, Yazd, Iran;1. Department of Psychology, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK;2. Engineering and the Environment, University of Southampton, UK |
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| Abstract: | Human energy consumption has gradually increased greenhouse gas concentrations and is considered the main cause of global warming. Currently, the building sector is a major energy consumer, and its share of energy consumption is increasing because of urbanization. This paper presents a framework for smart grid big data analytics and components required for an energy-saving decision-support system. The proposed system has a layered architecture that includes a smart grid, a data collection layer, an analytics bench, and a web-based portal. A smart metering infrastructure was installed in a residential building to conduct an experiment for evaluating the effectiveness of the proposed framework. Furthermore, a novel hybrid nature-inspired metaheuristic forecast system and a dynamic optimization algorithm are designed behind the analytics bench for achieving accurate prediction and optimization of future energy consumption. The main contribution of this study is that an innovative framework for the energy-saving decision process is presented; the framework can serve as a basis for the future development of a full-scale smart decision support system (SDSS). Through the identification of consumer usage patterns, the SDSS is expected to enhance energy use efficiency and improve the accuracy of future energy demand estimates. End users can reduce their electricity costs by implementing the optimal operating schedules for appliances, which are provided by the SDSS. |
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