论文标题
AI冷却器：基于IoT云的开放式机器学习框架，用于通过大数据分析BMS和环境数据的大数据分析来节省HVAC系统的能源
AI Chiller: An Open IoT Cloud Based Machine Learning Framework for the Energy Saving of Building HVAC System via Big Data Analytics on the Fusion of BMS and Environmental Data
论文作者
论文摘要
能源节省和减少建筑物的碳排放是打击气候变化的关键措施之一。供暖，通风和空调（HVAC）系统占建筑环境中大部分能源消耗，其中冷却器厂构成最高部分。在机械工程和建筑物服务域中对冷却器系统功耗的优化已经进行了广泛的研究。许多作品采用来自领域知识的物理模型。随着大数据和AI的发展，将机器学习涉及到优化问题的过程变得流行。尽管许多研究工作和项目转向节省节能的方向，但应用于优化问题仍然是一项具有挑战性的任务。这项工作旨在概述有关应如何进行节能的框架，如果应使用整体或单独建模，如何进行优化，为什么必须在初始部署处进行优化，为什么必须使用逐渐增加变化策略的数据模式。提出了对历史数据和实时数据实验实验的分析结果。
Energy saving and carbon emission reduction in buildings is one of the key measures in combating climate change. Heating, Ventilation, and Air Conditioning (HVAC) system account for the majority of the energy consumption in the built environment, and among which, the chiller plant constitutes the top portion. The optimization of chiller system power consumption had been extensively studied in the mechanical engineering and building service domains. Many works employ physical models from the domain knowledge. With the advance of big data and AI, the adoption of machine learning into the optimization problems becomes popular. Although many research works and projects turn to this direction for energy saving, the application into the optimization problem remains a challenging task. This work is targeted to outline a framework for such problems on how the energy saving should be benchmarked, if holistic or individually modeling should be used, how the optimization is to be conducted, why data pattern augmentation at the initial deployment is a must, why the gradually increasing changes strategy must be used. Results of analysis on historical data and empirical experiment on live data are presented.
