AI-Driven Renewable Energy Consumption Prediction and Optimization System

Attaining sustainability and energy efficiency in contemporary power grids has led to an increasing reliance on renewable energy sources.Unfortunately, excessive dependence on renewable sources such as solar, wind, and hydro makes energy generation more sensitive to the weather.Based on data analysis, machine learning algorithms, and real-time monitoring, the present study develops an AI-enabled energy usage prediction model that can improve energy efficiency.The system brings alarms on board, energy savings estimation, interfacing dashboards, and forecasts from linear regression to notify the industry and energy management.The accuracy of forecasting models may support grid stability, reducing operational costs, and improving energy efficiency, the findings show.