Artificial Intelligence and Machine Learning for Real-time Energy Demand Response and Load Management


  • Abdulgaffar Muhammad Department of Business Administration, Ahmadu Bello University, Nigeria
  • Aisha Ahmad Ishaq Department of Business Administration, Kano State Polytechnic Nigeria
  • Igbinovia Osaretin B Nile University
  • Mohammed Bello Idris Kaduna State University, Nigeria



Artificial intelligence, Machine learning, Real-time energy demand response, Load management, Energy consumption optimization, Renewable energy resources


Within this compendium, an exhaustive examination is undertaken to scrutinize the intricate amalgamation of artificial intelligence (AI) and machine learning (ML) techniques within the paradigm of real-time energy demand response and load management. Placing paramount importance on the pervasive significance of AI and ML, this research expounds upon their profound capabilities to adroitly harmonize the delicate interplay between supply and demand, meticulously calibrate the multifarious dimensions of grid stability, and optimize the boundless potential inherent in renewable energy resources. An in-depth analysis ensues, encompassing the deployment of AI algorithms, poised at the vanguard of demand response optimization, and the judicious utilization of ML techniques, flawlessly calibrated to deliver unerring accuracy across varying temporal scales in the realm of load forecasting. Furthermore, the seamless integration of AI into the very fabric of intelligent appliances and Internet of Things (IoT)-enabled systems unfolds, illuminating the path towards energy consumption optimization, ascertaining an intricate tapestry of interconnected devices, and engendering an ecosystem of intelligent load management. Notably, this comprehensive exposition delves into the far-reaching implications for optimal load management and resource allocation, magnifying the transformative potential that AI-driven algorithms hold in precisely balancing energy utilization and deftly managing the intricate interdependencies that permeate load distribution. Through meticulous elucidation, this illuminating manuscript emboldens the reader with insights into the progressive advancements and myriad benefits that the tandem of AI and ML confers upon the dynamic energy sector, charting an unyielding course towards unprecedented resilience and sustainable utilization of our cherished renewable energy resources.



DOI: 10.56556/jtie.v2i2.537

How to Cite

Muhammad, A., Ishaq, A. A., B, I. O., & Idris, M. B. (2023). Artificial Intelligence and Machine Learning for Real-time Energy Demand Response and Load Management. Journal of Technology Innovations and Energy, 2(2), 20–29.



Research Articles