IoT Based Utility Consumption Trend Prediction and Usage Management

This study presents the design, development, and evaluation of an advanced utility consumption monitoring and prediction platform aimed at facilitating efficient energy management within residential/commercial/industrial/campus settings. Using techniques such as ARIMAX modeling and Deep Q-Network (DQN) reinforcement learning, the platform offers a user-friendly interface for real-time monitoring, accurate forecasting, and intelligent usage management of electricity, water, and gas consumption. The DQN model achieved a Mean Absolute Percentage Error (MAPE) of 7.5%, significantly outperforming recent models. This demonstrates the superior accuracy and reliability of our system in trend prediction. Additionally, the platform provides enhanced energy management features, enabling users to reduce costs and improve energy efficiency. Through complex algorithms and multiple evaluation, this platform can play a significant role in promoting energy conservation and sustainability efforts in various settings.