Non intrusive load monitoring (NILM), or energy disaggregation, is the process of separating the total electricity consumption of a building as measured at single point into the building's constituent loads. Previous research in the field has mostly focused on residential buildings, and although the potential benefits of applying this technology to commercial buildings have been recognised since the field's conception, NILM in the commercial domain has been largely unexplored by the academic community. As a result of the heterogeneity of this section of the building stock (i.e., encompassing buildings as diverse as airports, malls and coffee shops), and hence the loads within them, many of the solutions developed for residential energy disaggregation do not apply directly. In this paper we highlight some insights for NILM in the commercial domain using data collected from a large smart meter deployment within an educational campus in Delhi, India, of which a subset of the data has been released for public use. We present an empirical characterisation of loads in commercial buildings, highlighting the differences in energy consumption and load characteristics between residential and commercial buildings. We assess the validity of the assumptions generally made by NILM solutions for residential buildings when applied to measurements from commercial facilities. Based on our observations, we discuss the required traits for a NILM system for commercial buildings, and run benchmark residential NILM algorithms on our data set to confirm our observations. To advance the research in commercial buildings energy disaggregation, we release a subset of our data set, called COMBED (commercial building energy data set).
A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks
Occupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads
为了将建筑物运营有效地整合到电网不断发展的需求响应计划中，关于使用建筑物电器进行电网服务的实时决策必须优于多个标准，从附加值到居住者的舒适度到电网服务的质量。在本文中，我们提出了一个数据驱动的决策支持框架，动态排名负载控制方案在商业建筑，解决多个决策标准（如居住舒适度，网格服务质量）的需求下，占用模式的不确定性。我们采用了一种随机多标准决策算法，最近应用于优先考虑住宅开/关负载，并将其扩展到i）复杂的负载控制决策（例如，调光的灯光，改变区域温度设定点）在商业建筑;和ii）分区占用模式的系统集成，以准确地识别短期电网服务机会。我们评估的性能框架削减空调，照明和插头负载在一个多区域的办公楼的一系列设计选择。一个原型系统，集成了一个互动的\textit{Data Analytics and Visualization}前端的帮助下，我们展示了一种方法，为建筑运营商监测能源消耗的灵活性，并通过解释排名背后的理由，发展信任的决策建议。
Machine Learning for Smart and Energy-Efficient Buildings
University of California, Berkeley（加州大学伯克利分校）；Xi’an Jiaotong University（西安交通大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；National University of Singapore（新加坡国立大学）
