暖通空调
计算机科学
学习迁移
信息隐私
楼宇自动化
需求响应
楼宇管理系统
数据建模
空调
机器学习
人工智能
工程类
计算机安全
电
数据库
热力学
电气工程
物理
机械工程
控制(管理)
作者
Zhenyi Wang,Peipei Yu,Hongcai Zhang
标识
DOI:10.1109/tsg.2022.3231592
摘要
Heating, ventilation, and air conditioning (HVAC) systems in buildings have great potential to provide regulation capacity that is leveraged to maintain the balance of supply and demand in the power system. In order to make full use of HVAC's regulation capacity, it is important to accurately evaluate it ahead of time. Because physical model-based approaches are hard to implement and highly personalized for each building, data-driven approaches are preferable for this capacity evaluation. However, given the insufficient data for individual buildings and buildings' potential unwillingness to share their data because of privacy concerns, it is extremely challenging to build a high-performance data-driven regulation capacity evaluation model. In this paper, we propose a privacy-preserving framework that combines federated learning and transfer learning to evaluate the regulation capacity of HVAC systems in heterogeneous buildings. Specifically, a classified federated learning algorithm is proposed to build capacity evaluation models of HVAC systems for different building types. Each building trains its model locally without sharing data with other buildings to preserve privacy. The algorithm also tackles data insufficiency and achieves high evaluation accuracy. In addition, we design a cross-type transfer learning algorithm to enhance model generalization and further address data deficiency. A protocol is created for the above two algorithms to protect privacy and security. Finally, numerical case studies are conducted to validate the proposed framework.
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