Reinforcement Learning-Based Energy Optimization for Smart Cities

Abstract

With rapid urbanization, energy demand in smart cities is increasing exponentially, necessitating efficient energy management solutions. Traditional energy optimization techniques rely on predefined rules, which fail to adapt to dynamic real-time variations. Reinforcement Learning (RL) has emerged as a promising AI-driven approach for optimizing energy distribution and consumption by learning from real-time data. This study explores the potential of RL in managing energy grids efficiently, reducing wastage, and integrating renewable energy sources. This research aims to develop an RL-based energy optimization model for smart cities, balancing energy demand and supply while integrating renewable energy sources to enhance sustainability and efficiency. The study utilizes reinforcement learning algorithms, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to optimize energy allocation.
The Hourly Energy Demand, Generation, and Weather Data dataset from Kaggle is used for training and validation, covering electrical consumption, generation, pricing, and weather data. The dataset is used for training and validation, covering energy consumption patterns in urban environments. The model is fine-tuned using reward shaping, hyperparameter optimization, and real-world constraints. The proposed RL-based model achieved an optimization efficiency of 92.34%, significantly improving energy utilization and reducing wastage compared to rule-based methods. The model dynamically adapts to varying energy demands, demonstrating robustness in real-time scenarios. This study highlights the effectiveness of RL-based energy optimization for smart cities, showcasing its potential in achieving sustainable energy management. The findings provide valuable insights for policymakers, urban planners, and energy providers in developing intelligent and adaptive energy distribution systems.