Article: Smart grid power demand and supply scheduling to preserve energy wastage using machine learning technique Journal: International Journal of Power and Energy Conversion (IJPEC) 2026 Vol.17 No.2 pp.192 - 212 Abstract: At present the demand for electrical energy is growing continuously because of the increasing population with their need for electricity. Additionally, to fulfil this need more and more power is being generated by using different energy generation resources. However, it is currently sufficient for our needs but shortly, we need to advance our ways of power generation and management. By the influence of this complexity, this paper is motivated to design and develop a deep learning model-assisted intelligent power demand and supply management system. In this context, two main types of power consumers are assumed and then the subcategories of consumers have been defined. Here, for each power consumption behaviour, a neural network model has been trained and future power demand has been predicted. Further, the predicted power demand is converted into the required amount of power supply. Based on power demand and supply consequences of 33 hours, the experimental results have been measured in terms of hit and miss ratio. Based on the experimental results the proposed model has provided an 87.5% hit ratio for best case and 62.5% hit ratio for worst case scenarios. Inderscience Publishers - linking academia, business and industry through research

Title: Smart grid power demand and supply scheduling to preserve energy wastage using machine learning technique

Authors: Jitendra Managre; Namit Gupta

Addresses: Electrical and Electronics Engineering, Shri Vaishnav Institute of Technology and Science, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, Madhya Pradesh, Pin-453111, India ' Electrical and Electronics Engineering, Shri Vaishnav Institute of Technology and Science, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, Madhya Pradesh, Pin-453111, India

Abstract: At present the demand for electrical energy is growing continuously because of the increasing population with their need for electricity. Additionally, to fulfil this need more and more power is being generated by using different energy generation resources. However, it is currently sufficient for our needs but shortly, we need to advance our ways of power generation and management. By the influence of this complexity, this paper is motivated to design and develop a deep learning model-assisted intelligent power demand and supply management system. In this context, two main types of power consumers are assumed and then the subcategories of consumers have been defined. Here, for each power consumption behaviour, a neural network model has been trained and future power demand has been predicted. Further, the predicted power demand is converted into the required amount of power supply. Based on power demand and supply consequences of 33 hours, the experimental results have been measured in terms of hit and miss ratio. Based on the experimental results the proposed model has provided an 87.5% hit ratio for best case and 62.5% hit ratio for worst case scenarios.

Keywords: smart grid; machine learning; power demand; consumer profile; convolutional neural network; CNN; internet of things; IoT; mean square error; MSE.

DOI: 10.1504/IJPEC.2026.152961

International Journal of Power and Energy Conversion, 2026 Vol.17 No.2, pp.192 - 212

Received: 12 Feb 2024
Accepted: 26 Aug 2024
Published online: 17 Apr 2026 *

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Title: Smart grid power demand and supply scheduling to preserve energy wastage using machine learning technique

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