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

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

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

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

Development of an office tenant electricity use model and its application for right-sizing HVAC equipment

As a consequence of considerable uncertainty about occupancy, occupant behaviour, and the corresponding effect on thermal loads in buildings, it is difficult to correctly size heating, ventilation, and air-conditioning (HVAC) equipment. Mechanical engineers avoid liability of potential under-capacity and corresponding thermal discomfort by making conservative assumptions about occupants. Meanwhile, there has been a surge in research on characterizing occupants through increasingly advanced modelling approaches to support building performance simulation, but these have focused on agent-based models representing individual occupants, which may be impractical for building-level HVAC equipment sizing. This paper describes the development of a data-driven stochastic tenant model using 15 months of data from 17 independent commercial tenants. The model is implemented in EnergyPlus to examine its potential for an improved HVAC equipment-sizing procedure. The results show: the standard schedules are reasonable though conservative; oversizing equipment does not greatly improve comfort; and the tremendous importance of modelling inter-tenant diversity

Text-mining building maintenance work orders for component fault frequency

Operators’ work order descriptions in computerized maintenance management systems (CMMS) represent an untapped opportunity to benchmark a facility’s maintenance and operation performance. However, it is challenging to carry out analytics on these large and amorphous databases. This paper puts forward a text-mining method to extract information about failure patterns in building systems and components from CMMS databases. The method is executed in three steps. Step 1 is pre-processing to convert work order descriptions into a mathematical form that lends itself to a quantitative lexical analysis. Step 2 is clustering to focus on interesting sections of a CMMS database that contain work orders about failures in building systems and components – rather than less interesting routine maintenance and inspection activities. Step 3 is association rule-mining to identify the coexistence tendencies among the terms of cluster of interest (e.g. coexistence of the terms ‘radiator’ and ‘leak’). This text-mining method is demonstrated by using two data sets. One data set was from a central heating and cooling plant with four boilers and five chillers; the other data set was from a cluster of 44 buildings. The results provide insights into per equipment breakdown of failure events, top system and component-level failure modes, and their occurrence frequencies

Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review

A primary strategy for the energy-efficient operation of commercial office buildings is to deliver building services, including lighting, heating, ventilating, and air conditioning (HVAC), only when and where they are needed, in the amount that they are needed. Since such building services are usually delivered to provide occupants with satisfactory indoor conditions, it is important to accurately determine the occupancy of building spaces in real time as an input to optimal control. This paper first discusses the concepts of building occupancy resolution and accuracy and briefly reviews conventional (explicit) occupancy detection approaches. The focus of this paper is to review and classify emerging, potentially low-cost approaches to leveraging existing data streams that may be related to occupancy, usually referred to as implicit/ambient/soft sensing approaches. Based on a review and a comparison of related projects/systems (in terms of occupancy sensing type, resolution, accuracy, ground truth data collection method, demonstration scale, data fusion and control strategies) the paper presents the state-of-the-art of leveraging existing occupancy-related data for optimal control of commercial office buildings. It also briefly discusses technology trends, challenges, and future research directions

Characterization of a Building's operation using automation data: A review and case study

This paper presents a critical review of the automated on-going commissioning (AOGC) methods for air-handling units (AHU) and variable air volume terminal (VAV) units in commercial buildings. The common faults studied in the literature were identified. The diagnostic approaches taken and the characteristics of the fault-symptom datasets utilized were categorized. It was found that the diagnostics methods were vastly fragmented, and most of them employed pure-statistical approaches. Only a few studies attempted to assimilate the automation data within the underlying physical processes. In addition, a large fraction of the reviewed literature has been devoted to physical faults in AHUs. Only a few studies were conducted to diagnose faults-related with controls programming and faults at the zone level. Upon the literature survey findings, an inverse greybox modelling-based AOGC approach was put forward. Its strengths and weaknesses were demonstrated through a case study conducted using the archived building automation system (BAS) data of an office building in Ottawa, Canada. The results of this case study indicate that inverse greybox modelling-based AOGC is a promising method to diagnose both physical and controls programming related faults at AHUs and VAVs