Seminar 28 -- Application of Machine Learning in Future Proofing Building Operation

This product is a zip file that contains files that consist of PowerPoint slides synchronized with the audio-recording of the speaker, PDF files of the slides, and audio only (mp3 format) as noted.

Advanced building control and operation provide the smartness for a building to address the many challenges faced by the industry. High-fidelity energy forecasting model, as a core component of model-predictive-control and energy analytics, is critical for advanced building operation. Data-driven energy forecasting modeling, especially those that use machine learning methods, receives great interest recently due to its cost-effectiveness and scalability. This seminar presents several case studies using various machine learning methods to demonstrate the effectiveness and performance of machine learning enabled energy forecasting and their application in advanced building operation used for future proofing operation.

1. Estimation of HVAC Energy Consumption Using Feature Selection and Machine-Learning Approaches< /br>Zheng O'Neill, Ph.D., P.E., Member, Texas A&M University, College Station, TX
2. Short-Term Load Forecasting to Enable Better Control of Buildings-to-Grid Integration< /br>Bing Dong, Ph.D., Associate Member, Syracuse University, Syracuse, NY