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Application of deep learning for energy management in smart grid
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- Banerjee, Sourasekhar (author)
- Umeå universitet,Institutionen för datavetenskap
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- Ghosh, Soumitra (author)
- Department of Computing Science and Engineering, Indian Institute of Technology, Patna, India
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- Mishra, Brojo Kishore (author)
- GIET University, Gunupur, India
- (creator_code:org_t)
- 2021-08-12
- 2022
- English.
- In: Deep learning in data analytics. - Cham : Springer. - 9783030758547 - 9783030758554 ; , s. 221-239
- Book chapter (peer-reviewed)
Abstract
Subject headings
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- In the modern electronic power system, energy management and load forecasting are important tasks. Energy management systems are designed concerning monitoring and optimizing the energy requirement in smart systems. This research work is divided into two parts. The first part will contain load forecasting and energy management in a smart grid. Load forecasting in the smart grid can be divided into three parts long-term, mid-term, and short-term load forecasting. The second part will describe energy usage optimization for the electric vehicle. Here we will show grids to vehicle energy demand management and optimization. This chapter will first introduce different deep learning techniques and then discuss their applications related to smart-grid and smart vehicle.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Naturresursteknik -- Energisystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Environmental Engineering -- Energy Systems (hsv//eng)
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- ref (subject category)
- kap (subject category)
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