讲座题目：Building Energy Modeling: A meta-learning based Approach
主讲人：崔璨 博士 (Intel研究中心，数据科学家，美国加州)
主持人：马利军 副教授 管理科学系副主任
Buildings consume nearly 50% of the total energy in the U.S., which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings.
An integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a general modeling approach for various types of buildings. A number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency.
Can Cui is a Data Scientist working at the Research Lab of Intel Corporation, California, United States. She is currently focusing on Sales and Marketing data analytics and insights generation. She was graduated from Industrial Engineering PhD program, Arizona State University, 2016. She got the B.S. degree of Transportation Management from Beijing Jiaotong University, 2010. Her specialties are data analytics, predictive modeling, big data processing and time series forecasting. She has been working on a project sponsored by the United States Department of Energy for 5+ years. The project is to improve the building energy consumption modeling by using data-driven predictive modeling techniques and machine learning methods. Her publications appear in Applied Energy, Expert Systems with Applications and INFORMS conferences.