Energy Optimization in Smart Cities using Reinforcement Learning

Author(s)	Mr. Shubham Singh
Country	India
Abstract	The increasing complexity of urban infrastructures in smart cities has created an urgent need for intelligent energy management systems that can dynamically adapt to changing environmental and consumption patterns. This research proposes the development of a Reinforcement Learning (RL)–based framework for optimizing energy utilization in smart grids, street lighting, and building automation. Unlike traditional rule-based systems, the RL agent will learn optimal energy distribution strategies through continuous interaction with real-time data, ensuring improved efficiency and sustainability. The project aims to implement and evaluate algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) using simulated energy datasets. Expected outcomes include a significant reduction in energy wastage, operational costs, and carbon emissions while maintaining service reliability. The proposed research aligns with the Digital India and Smart Cities Mission and contributes to the broader goal of sustainable urban development through intelligent, self-learning energy management systems.
Keywords	Reinforcement Learning, Smart Cities, Energy Optimization, Artificial Intelligence, IoT, Sustainability
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-23

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research