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Abstract:
AI is transforming energy management, offering Energy Managers, TAB professionals, Cx agents, and Facility Managers powerful tools for efficiency and cost reduction. This presentation highlights practical, data-driven applications that reveal energy-saving opportunities, optimize cooling, and reduce operational waste across individual buildings and large portfolios.
Attendees will explore real-world examples, including a fast-food chain case study demonstrating how these solutions yield measurable savings and operational improvements.
Learning Objectives:
- Understanding Key Challenges in Developing Meaningful Machine Learning Models for Energy Management
- Leveraging Explainable AI to Generate Actionable Insights for Energy Savings
- Exploring Anomaly Detection Techniques and Their Impact on Energy Efficiency
- Scalability and Cost-Effectiveness of AI Solutions for Large Building Portfolios
Sponsored by: Energy Twin
Energy Twin specializes in advanced machine learning solutions for energy management. Beyond its primary offerings for building portfolio analysis, the company provides customized machine learning solutions tailored to specific client needs, enabling diverse organizations to optimize energy consumption and reduce operational costs. With a focus on scalability and efficient energy use, Energy Twin enables clients to drive impactful energy savings through accessible, data-driven insights.