AI-DRIVEN ENERGY LOAD FORECASTING AND RENEWABLE INTEGRATION: OPTIMIZING SUSTAINABILITY FOR SMART CAMPUSES

This study explores advanced data-driven methodologies for forecasting electricity demand and integrating renewable energy resources, with a focus on Cornell University’s campus infrastructure. Leveraging historical data from energy management systems and regional meteorological records, we developed predictive models to analyze energy consumption patterns and renewable energy generation potential. Techniques such as Long Short-Term Memory (LSTM) networks, ARIMA, Random Forest, and Generative Adversarial Networks (GANs) were employed to capture temporal dependencies and enhance forecasting accuracy. Clustering algorithms, including k-means and Expectation-Maximization (EM), provided insights into energy usage behaviors across different building types and climatic conditions. Our findings reveal significant seasonal and hourly trends in solar and wind energy generation, with complementary patterns that support hybrid renewable energy systems. Predictive models demonstrated high accuracy, enabling the estimation of additional renewable capacity and the design of energy storage solutions to mitigate intermittency challenges. The study highlights the scalability of these methods to other campuses or urban settings and their potential to contribute to carbon neutrality goals. By integrating machine learning with renewable energy management, this research advances the development of sustainable, efficient, and resilient energy systems.