Abstract
Smart Grid is emerging as one of the most promising technologies that will provide several improvements over the traditional power grid. Providing availability is a significant concern for the power sector, and to achieve an uninterrupted power supply accurate forecasting is essential. In the implementation of the future Smart Grid, efficient forecasting plays a crucial role, as the electric infrastructure will work, more and more, by continuously adjusting the electricity generation to the total end-use load. Electricity consumption depends on a vast domain of randomly fluctuating influential parameters, and every region has its own set of parameters depending on the demographic, socioeconomic, and climate conditions of that region. Even for the same set of parameters, the degree of influence on power consumption may vary over different sectors, like, residential, commercial, and industrial. Thus it is essential to quantify the dependency level for each parameter. We have proposed a generalized mid-term forecasting model for the industrial sector to predict the quarterly energy usage of a vast geographic region accurately with a diverse range of influential parameters. The proposed model is built and tested on real-life datasets of industrial users of various states in the U.S.
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Acknowledgments
This research work is supported by the projects “ADditive Manufacturing & Industry 4.0 as innovation Driver (ADMIN 4D)” and TEQIP Phase 3 in University of Calcutta (UCT-CU). Authors sincerely acknowledge and thank the projects for providing the support required for carrying out the research work.
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Chakraborty, M., Banerjee, S., Chaki, N. (2020). A Framework Towards Generalized Mid-term Energy Forecasting Model for Industrial Sector in Smart Grid. In: Hung, D., D´Souza, M. (eds) Distributed Computing and Internet Technology. ICDCIT 2020. Lecture Notes in Computer Science(), vol 11969. Springer, Cham. https://doi.org/10.1007/978-3-030-36987-3_19
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