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Nazir Ahmad Parray is a Research Scholar in the Department of Mathematical Sciences at APS University, Rewa, Madhya Pradesh, India. His research interests include Mathematical Modeling, Machine Learning, Optimization Techniques, and Industrial Analytics.
Industrial sectors constitute a substantial share of global energy consumption, rendering energy efficiency essential for sustainable development and the reduction of operational costs. Conventional energy management strategies frequently fail to address the intricacies of contemporary industrial processes and the extensive data produced by smart manufacturing systems. Deep Learning (DL) has developed as a potent tool for analysing industrial energy data, predicting consumption trends, optimising resource allocation, and facilitating informed decision-making. Industries can attain real-time energy optimisation and predictive energy management by utilising advanced models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformer architectures. This chapter examines the principles, designs, applications, problems, and future trajectories of deep learning-based energy optimisation systems, emphasising their contribution to enhancing industrial sustainability, productivity, and operational efficiency.