Exploring occupants\u27 impact at different spatial scales

Buildings\u27 users have widely been accepted as a source of uncertainty in building energy performance predictions. However, it is not evident that the diversity of occupants\u27 presence and behavior at the building level is as important as at the room level. The questions are: How should occupants be modeled at different spatial scales? At the various scales of interest, how much difference does it make if: (1) industry standard assumptions or a dynamic occupant modeling approach is used in a simulation-based analysis, and (2) probabilistic or deterministic models are used for the dynamic modeling of occupants? This paper explores the reliability of building energy predictions and the ability to quantify uncertainty associated with occupant modeling at different scales. To this end, the impacts of occupancy and occupants\u27 use of lighting and window shades on the predicted building lighting energy performance at the room and building level are studied. The simulation results showed that the inter-occupant variation at larger scales is not as important as at the room level. At larger scales (about 100 offices), the rule-base model, custom schedule model, and stochastic lighting use model compared closely for predicting mean annual lighting energy use

Data-driven short-term load forecasting for heating and cooling demand in office buildings

Short-term forecasts of energy demand in buildings serve as key information for various operational schemes such as predictive control and demand response programs. Despite this, developing forecast models for heating and cooling loads has received little attention in the literature compared to models for electricity load. In this paper, we present data-driven approaches to forecast hourly heating and cooling energy use in office buildings based on temporal, autoregressive, and exogenous variables. The proposed models calculate hourly loads for a horizon between one hour and 12 hours ahead. Individual models based on artificial neural networks (ANN) and change-point models (CPM) as well as a hybrid of the two methods are developed. A case study is conducted based on hourly thermal load data collected from several office buildings located on the same campus in Ottawa, Canada. The models are trained with more than two years of hourly energy-use data and tested on a separate part of the dataset to enable unbiased validation. The results show that the ANN model can achieve higher forecasting accuracy for the longest forecast horizon and outperforms the results obtained by a Naïve approach and the CPM. However, the performance of the hybrid CPM-ANN method is superior compared to individual models for all studied buildings

The contextual factors contributing to occupants' adaptive comfort behaviors in offices - A review and proposed modeling framework

Occupants play an unprecedented role on energy use of office buildings and they are often perceived as one of the main causes of underperforming buildings. It is therefore necessary to capture the factors influencing these energy intensive occupant behaviors and to incorporate them in building design. This review-based article puts forward a framework to represent occupant behavior in buildings by arguing: occupants are not illogical and irrational but rather that they attempt to restore their comfort in the easiest way possible, but are influenced by many contextual factors. This framework synthesizes statistical and anecdotal findings of the occupant behavior literature. Furthermore, it lends itself to occupant behavior researchers to form a systematic way to report the influential contextual factors such as ease of control, freedom to reposition, and social constra ints

Cluster analysis-based anomaly detection in building automation systems

Faults in heating, ventilation, and air-conditioning control networks substantially affect energy and comfort performance in commercial buildings. As these control networks are comprised of many sensors and actuators, it is challenging to identify, often subtle, anomalies caused by these faults. In this paper, we develop a cluster analysis method for anomaly detection. The proposed method consolidates the building automation system data into a small number of distinct patterns of operation. These distinct patterns help energy managers discover and interpret anomalies through visualization of these patterns. The method was demonstrated with a year's worth of building automation system data from 247 thermal zones and an air handling unit. Anomalies associated with zone temperature and airflow control were identified in about one-third of these zones. At the air handling unit-level, we identified anomalies related with three different faults: the use of economizer mode with perimeter heating, and leaky outdoor and return air dampers. The use of economizer mode with perimeter heating affected 39% to 52% of the total operation period and caused the outdoor air damper to remain fully open and the heat recovery unit to remain off during most of the heating season

Do building energy codes adequately reward buildings that adapt to partial occupancy?

Most building energy codes’ performance path implicitly reward buildings that perform well under steady and near-capacity occupancy conditions, even though these are not typical operating conditions. Partially, as a result, our buildings tend not to be designed with features that allow optimal energy performance in the common circumstance of partial and fluctuating occupancy. The objective of this paper is to examine current building energy codes in this regard. The paper uses a simulation-based approach to demonstrate two strategies that target occupancy-adaptability: demand-controlled ventilation and smaller-than-required lighting control zones with occupancy-controlled lighting. In this paper, a code-compliant EnergyPlus archetype office building in Toronto, Canada was used to evaluate these design features under different occupancy conditions. The results confirm that both demand-controlled ventilation and smaller lighting control zones are most advantageous at lower occupancy levels. Accordingly, the paper concludes with generalized recommendations for code modifications to properly credit buildings with greater adaptability to partial occupancy

A review of data collection and analysis requirements for certified green buildings

While the research community widely recognizes the importance of post-occupancy measurement of buildings to verify performance, requirements of green building certification schemes are highly varied. To assess the effectiveness of building certification schemes during the operation and maintenance stage of certified buildings, this paper critically reviews recent case studies that report on post-certification performance. The review of relevant case studies from the literature reveals some important findings in relation to the performance gap of certified buildings. Subsequently, major operation and maintenance-related building certification schemes are surveyed to reveal the underlying reasons behind this performance gap. Post-certification actions that require post-occupancy data collection and analysis are identified through this survey and compared to highlight their strengths and shortcomings and pinpoint the major discrepancies in data infrastructure and archiving practices that hinder certified buildings in performing according to their design intent. Lastly, suggestions are extracted which may shed light on the re-certification pathways

A method for extracting performance metrics using work-order data

Holistic performance metrics are necessary to understand how operational resources are used and to detect anomalous zones, floors, equipment, and work-order categories in large commercial and institutional buildings. Work-order data in computerized maintenance management systems (CMMS) represent an untapped potential to extract such performance metrics. In this paper, a method to conduct text analytics on CMMS data is developed and demonstrated through a case study in which four years’ worth of data from four large commercial buildings are used. Association rule mining technique is employed to identify building, system, and component-level recurring work-order taxonomies and common failure modes. The results highlight the potential of kernel density functions, decision trees, Sankey diagrams, survival curves and stacked line plots to effectively visualize the temporal, spatial, and categorical anomalies in the complaint patterns. It is identified that often only a few floors and complaint types account for most of the complaints in a building. The analysis of operator comments reveal that the most frequent lighting-related complaints are resolved by replacing ballasts and lights, and the thermal and air quality complaints are addressed by adjusting the temperature setpoints, airflow rates, and fan operation schedules

Data analytics to improve building performance: A critical review

The data inherent in building automation systems, computerized maintenance management systems, security and access control systems, and IT networks represent an untapped opportunity to improve the operation and maintenance (O&M) of buildings. This paper reports the findings of a critical review of the literature regarding the use of data analytics in building O&M applications, and a two-day stakeholder's workshop titled Big Data in Building Operations. Building on the discussions at the workshop and the literature survey, the current state of the O&M related decision-making process was identified: the data availability in existing buildings was discussed; the challenges related with accessing and processing these datasets were examined; and emerging sensing technologies were presented. Further, the research fields applying data analytics in O&M were introduced, the barriers to their widespread use in practice were discussed, future work recommendations were developed; and the need for semantic models of O&M data and comprehensive open O&M datasets was identified for the development and assessment of data analytics-driven energy and comfort management algorithms