This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

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Buildings are one of the largest consumers of electricity. Dominant electricity consumption within the buildings, contributed by plug loads, lighting and air conditioning, can be significantly improved using Occupancy-based Building Management Systems (Ob-BMS). In this paper, we address three critical aspects of Ob-BMS i.e. 1) Modular sensor node design to support diverse deployment scenarios; 2) Building architecture to support and scale fine resolution monitoring; and 3) Detailed analysis of the collected data for smarter actuation. We present key learning across these three aspects evolved over more than one year of design and deployment experiences.The sensor node design evolved over a period of time to address specific deployment requirements. With an opportunity at the host institute where two dorm buildings were getting constructed, we planned for the support infrastructure required for fine resolution monitoring embedded in the design phase and share our preliminary experiences and key learning thereof. Prototype deployment of the sensing system as per the planned support infrastructure was performed at two faculty offices with effective data collection worth 45 days. Collected data is analyzed accounting for efficient switching of appliances, in addition to energy conservation and user comfort as performed in the earlier occupancy based frameworks. Our analysis shows that occupancy prediction using simple heuristic based modeling can achieve similar performance as more complex Hidden Markov Models, thus simplifying the analytic framework.

Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors.

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