AI-Powered Peak Demand Forecasting: How Machine Learning is Transforming Cooperative Energy Management

As distribution cooperatives face increasing pressure to manage peak demand more efficiently, traditional forecasting methods—relying on historical load profiles and weather data—often lead to inaccuracies and inefficiencies.

This session will explore an innovative research and development project leveraging artificial intelligence (AI) and machine learning (ML) to enhance peak demand forecasting. By integrating over two years of historical data, weather information, and real-time SCADA data, this ML-driven model provides real-time guidance through an intuitive dashboard and actionable reports, enabling utilities to make smarter, data-driven decisions.

Key benefits and discussion points include:

• Enhancing demand forecasting accuracy to reduce uncertainty.
• Minimizing risks associated with poorly timed demand response actions.
• Lowering wholesale power costs for cooperatives.
• Optimizing customer load-shedding programs for peak periods.
• Improving resource management and distribution network efficiency.

Attendees will gain practical insights into how AI and ML are redefining energy forecasting and empowering cooperatives to make more informed, cost-effective, and strategic decisions in a rapidly evolving energy landscape.