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Dr. Jyothi Gupta is an Associate Professor in the Department of Physics at Nitte Meenakshi Institute of Technology (NMIT), Nitte (Deemed to be University), Bengaluru, India. Her research interests include computational physics, Artificial Intelligence, smart energy systems, data analytics, and interdisciplinary scientific research. She actively participates in research integrating modern computing technologies with physical sciences and engineering applications.
The swift expansion of urbanisation, industrialisation, and digital evolution has markedly heightened global energy requirements, necessitating the development of sophisticated energy management systems that enhance consumption efficiency while promoting sustainability. Conventional energy management strategies frequently depend on past consumption trends and manual oversight, rendering them insufficient for addressing fluctuating energy demands and decentralised energy assets. The amalgamation of Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data Analytics, and Edge Computing has transformed smart energy management through real-time surveillance, predictive analysis, demand forecasting, and astute decision-making assistance. IoT sensors perpetually gather energy consumption information from residential, commercial, industrial, and smart grid settings, while Machine Learning (ML) and Deep Learning (DL) algorithms proficiently predict future energy requirements and suggest enhanced energy management techniques. This chapter elucidates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues for intelligent energy consumption forecasting through AI, IoT, and cloud computing.