Building Performance Monitoring Research Articles

“Do office workers’ comments corroborate the ratings they give their buildings?” A qualitative analysis of comments on operational factors in the workplace

PurposeThis paper aims to illustrate the extensive benefits of qualitative data analysis as a rarely undertaken process in post-occupancy evaluation surveys. As a result, there is limited evidence of what occupants say about their buildings, especially for operational parameters, as opposed to how they rate them. While quantitative analyses provide useful information on how workers feel about workplace operational factors, qualitative analyses provide richer information on what aspects of the workplace workers identify as influential to their comfort, well-being and productivity.Design/methodology/approachThe authors analysed 6,938 comments from office buildings worldwide on workers’ perception of workplace operational factors: design, storage, needs, space at desks and storage in their work environments. These factors were analysed based on the buildings’ design intent and use, and the associated comments were coded into positive, negative and balanced comments. The authors used a combination of coding, descriptive analysis, content analysis and word cloud to dissect the comments.FindingsThe findings showed that whereas workers rated these operational factors favourably, there were significantly more negative comments about each factor. Also, the Chi-square test showed a significant association (p < 0.01) between the satisfaction scale and the type of comments received for all the operational factors. This means that when a factor is rated high in the satisfaction score (5–7), there were fewer negative and more positive comments and vice versa. The word cloud analysis highlighted vital aspects of the office environment the workers mostly commented on, such as open plan design, natural lighting, space and windows, toilets, facilities, kitchens, meeting room booking systems, storage and furniture.Research limitations/implicationsThis study highlights the importance of dissecting building occupants’ comments as integral to building performance monitoring and measurement. These emphasise the richness and value of respondents’ comments and the importance of critically analysing them. A limitation is that only 6,938 comments were viable for analysis because most comments were either incomplete with no meaning or were not provided. This underlines the importance of encouraging respondents to comment and express their feelings in questionnaire surveys. Also, the building use studies questionnaire data set presents extensive opportunities for further analyses of interrelationships between demographics, building characteristics and environmental and operational factors.Practical implicationsThe findings from this study can be applied to future projects and facility management to maintain and improve office buildings throughout their life cycle. Also, these findings are essential in predicting the requirements of future workplaces for robust workplace designs and management.Originality/valueThe authors identified specific comments on the performance of workplaces across the globe, showing similarities and differences between sustainable, conventional, commercial and institutional buildings. Specifically, the analysis showed that office workers’ comments do not always corroborate the ratings they give their buildings. There was a significantly higher percentage of negative comments than positive comments despite the high satisfaction scores of the operational factors.

Data-informed building energy management (DiBEM) towards ultra-low energy buildings

To support building operations in reaching ultra-low energy targets, this paper proposes a data-informed building energy management (DiBEM) framework to improve energy efficiency systematically and continuously at the operation stage. Specifically, it has two key features including data-informed energy-saving potential identification and data-driven model-based energy savings evaluation. The paper demonstrates the proposed DiBEM with a detailed case study of an office and living laboratory building located in Cambridge, Massachusetts called HouseZero. It focuses on revealing the performance of the energy-efficient interventions from two-years’ building performance monitoring data, as well as evaluating energy savings from the interventions based on the data-driven approach. With Year 1 as baseline, several interventions are proposed for Year 2 including improvements to controls and operation settings, encouragement of occupants’ behavior for energy savings, and hardware retrofitting. These were deployed to heating/cooling, domestic hot water, lighting, plug and other loads, and photovoltaic (PV) systems. To quantify the impacts of different interventions on energy end uses, several data-driven models are developed. These models utilize linear regression, condition model, and machine learning techniques. Consequently, the heating/cooling energy consumption that was already ultra-low in Year 1 (12.8 kWh/m2) is further reduced to 9.7 kWh/m2 in Year 2, while the indoor thermal environment is well maintained. The domestic hot water energy is reduced from 2.3 kWh/m2 to 1.2 kWh/m2. The lighting energy is only increased from 0.3 kWh/m2 in pandemic operations without occupancy in Year 1 to 0.8 kWh/m2 in partial normal operations in Year 2, while the indoor illuminance level meets occupants’ requirements. Combined with other relatively constant loads and the reduction of plug and other loads due to COVID building operation restrictions, the total energy use intensity is thereby reduced from 54.1 kWh/m2 to 42.8 kWh/m2, where 5.4 kWh/m2 of energy reduction for Year 2 is estimated to be contributed by the energy-efficient interventions. PV generation is 36.1 kWh/m2, with an increase of 1.4 kWh/m2 from a new inverter. In summary, this paper demonstrates the use of DiBEM through a detailed case study and long-term monitoring data as evidence to achieve ultra-low energy operations.

A protocol for longitudinal monitoring of individual building occupants and their environments.

Buildings account for approximately 40% of the energy consumption across the European Union, so there is a requirement to strive for better energy performance to reduce the global impact of urbanised societies. However, energy performant buildings can negatively impact building occupants (e.g., comfort, health and/or wellbeing) due to a trade-off between airtightness and air circulation. Thus, there is a need to monitor Indoor Environmental Quality (IEQ) to inform how it impacts occupants and hence redefine value within building performance metrics. An individualised study design would enable researchers to gain new insights into the effects of environmental changes on individuals for more targeted e.g., health interventions or nuanced and improved building design(s). This paper presents a protocol to conduct longitudinal monitoring of an individual and their immediate environment. Additionally, a novel approach to environmental perception gathering is proposed that will monitor environmental factors at an individual level to investigate subjective survey data pertaining to the participant’s perceptions of IEQ (e.g., perceived air quality, thermal conditions, light, and noise). This protocol has the potential to expose time-differential phenomena between environmental changes and an individual’s behavioural and physiological responses. This could be used to support building performance monitoring by providing an interventional assessment of building performance renovations. In the future it could also provide building scientists with a scalable approach for environmental monitoring that focuses specifically on individual health and wellbeing.

Building performance monitoring and analysis of a large-span aerogel-membrane airport terminal

The utilization of large-span membrane structures as airport terminals has attracted considerable attention due to excellent building aesthetics, reasonable structural behavior and multi-functional applications. Safety and serviceability of such structures are strongly related to temperature effects in comparisons with steel/aluminum structures since temperature can accelerate material creep strain and deteriorate structural behavior. One alternative method to reduce temperature effects is the incorporation of aerogel materials between two membrane layers. This paper thus focuses on temperature effects of a newly-built aerogel-membrane airport terminal and utilizes experimental results to assess corresponding building performance.The aerogel-membrane airport terminal is designed with wind loads of 0.4 kN/m2, snow loads of 0.7 kN/m2 and earthquake magnitude of 7 in accordance with the requirements of building standards and codes. The aerogel-membrane roof is composed of an outer membrane layer and an inner composite membrane layer integrated aerogel material mats. The surface temperature gradients with respect to outer membrane layer, inner membrane layer and indoor air are identified and quantified. The quantified temperature can be used for thermal-structural analysis and building performance improvement in the design process. The calculated thermal performance indexes mean a hot environment in the inner airport space. A further comparative analysis with a two-layer membrane stadium demonstrates that aerogel materials can reduce temperature effects significantly and ensure structural safety. Therefore, the effects of aerogel on the improvement of structural behavior and building performance of a new membrane airport have been validated with experimental results and comparisons.

Building information modeling–based cyber-physical platform for building performance monitoring

Building performance management requires massive data input; however, the relevant data are separated and heterogeneous; thus, it prevents a comprehensive building performance management. Building information modeling brings a new way to capture rich information of a building, and has great potential in data interoperability for building performance management. This article presents a scalable building information modeling–based cyber-physical platform for building performance monitoring to integrate heterogeneous data from different buildings. A smart sensor network based on Arduino and standard protocol is installed for data sensing and collection. A building information modeling–based sensing information model integrating heterogeneous data in a unified structure is proposed, and a scalable NoSQL database is established to store data in a cloud environment. A series of RESTful web services is developed to share data for building performance management applications. The proposed platform is developed taking the advantage of horizontal scalability of NoSQL database, and the data schema and services are generated automatically based on the unified data model. The platform has collected data from 77 buildings in China, and the results of a case study show the platform brings a new paradigm in collecting, storing, integrating, and sharing of sensor data and building information for building performance monitoring and analytics.

Building performance monitoring: from in-situ measurement to regression-based approaches

Simple and robust data analysis methodologies are crucial to learn insights from measured data and reduce the performance gap in building stock. For this reason, continuous performance monitoring should become a more diffuse practice in order to improve our design and operation strategies for the future. The research presented aims to highlight potential links between experimental approaches for test-facilities and methods and tools used for continuous performance monitoring, at the state of the art. In particular, we explore the relation between ISO 9869:2014 method for in-situ measurement of thermal transmittance (U) and regression-based monitoring approaches, such as co-heating test and energy signature, for heat load coefficient (HLC) and solar aperture (gA) estimation. In particular, we highlight the robustness and scalability of these monitoring techniques, considering relevant issues in current integrated engineer design perspective. These issues include, among others, the necessity of limiting the number of a sensors to be installed in buildings, the possibility of employing both experimental and real operation data and, finally, the possibility to automate and perform monitoring at multiple scales, from single components, to individual buildings, to building stock and cities.

Monitored Indoor Environmental Quality of a Mass Timber Office Building: A Case Study

A broad range of building performance monitoring, sampling, and evaluation was conducted periodically after construction and spanning more than a year, for an occupied office building constructed using mass timber elements such as cross-laminated timber (CLT) floor and roof panels, as well as glue-laminated timber (GLT) beams and columns. This case study contributes research on monitoring indoor environmental quality in buildings, describing one of the few studies of an occupied mass timber building, and analyzing data in three areas that impact occupant experience: indoor air quality, bacterial community composition, and floor vibration. As a whole, the building was found to perform well. Volatile organic compounds (VOCs), including formaldehyde, were analyzed using multiple methods. Formaldehyde was found to be present in the building, though levels were below most recommended exposure limits. The source of formaldehyde was not able to be identified in this study. The richness of the bacterial community was affected by the height of sampling with respect to the floor, and richness and composition was affected by the location within the building. Floor vibration was observed to be below recognized human comfort thresholds.

Continuous monitoring of indoor environmental quality using an Arduino-based data acquisition system

Building performance monitoring could be limited due to the cost and inflexibility of hardware and software platforms for data acquisition. This paper describes a portable continuous measurement toolbox which provides a robust, easily extendable, and low-cost setup for indoor environmental quality (IEQ) monitoring and performance assessment. Various sensors—temperature, relative humidity, illuminance, CO2, VOC, PM2.5, and occupancy—for IEQ performance measurement are included in this toolbox. Arduino Uno boards were connected to the sensors for data acquisition. ZigBee communication protocol was established between an XBee device for each Arduino board and an XBee receiver connected to a computer. The toolbox utilized the open source, agent-based software platform VOLTTRON for data communication and analysis. The data collection system was calibrated against an accurate data acquisition card. Experiments have been conducted using the toolbox for assessing IEQ performance in an open computer lab within a commercial building. Thermal comfort, indoor air quality, and lighting performance have been analyzed based on collected data. The study demonstrated reliability and robustness of the toolbox for continuous monitoring of indoor environmental quality.

A longitudinal building fabric and energy performance analysis of two homes built to different energy principles

This paper reports on the building performance monitoring and annual energy demand of two homes built side-by-side over an occupancy period of three years. The study compares the results from on-site monitoring against the assumed parameters and calculations from compliance modelling at design stage. It focuses on the differences and impact of occupancy behaviour, weather conditions, quality of construction and operation which contribute to an increase in energy consumption creating a gap in performance between design and actual. The results from the study show disparities in the fabric performance reflecting on the overall consumption of energy. This longitudinal analysis highlights how building performance needs to be evaluated over longer periods in order to fully understand how homes and their occupants operate and consume energy. The impact of the real performance of homes in Scotland over longer periods needs to become standardised, and a mechanism for feedback into regulatory mechanisms and construction practices applied, if carbon emission targets are to be met.

Restoration of long-term missing gaps in solar radiation

Solar radiation is an important climatic variable and widely used in building performance monitoring and analysis. However, due to sensor malfunction, data transmission problems, and quality assurance issues, there are often short-term or long-term missing data on solar radiation. These gaps are challenging for engineers involved in building performance monitoring and control. This paper examines and compares three different approaches, namely, singular spectrum analysis (SSA), statistically adjusted solar radiation (SASR), and the temperature-based approach (TBA), for restoring missing solar data. The TBA, SASR, and SSA are applied to fill artificial gaps which are generated continuously as representative of up to 25 days missing data in actual hourly solar radiation data of Oklahoma City North (OKCN) for 2012 and Albuquerque for 2005. Results show that SSA outperforms the other methods for filling solar radiation gaps of up to 5 days. For gaps up to 20 days, the SSA and TBA have similar performance. For gaps larger than 20 days, the TBA is more suitable. The SASR performs similarly to the TBA for dry and sunny Alburquerque climates, but worse in OKCN. Accuracy of the SSA decreases with increasing gap lengths. The study concludes by recommending appropriate methods for different gap lengths.

Restoration of missing dry-bulb temperature data with long-term gaps (up to 60 days) for use in building performance monitoring and analysis—Part II

The lack of standard procedures for filling climatic data has the potential to undermine design, monitoring, and control efforts aimed at climate-responsive building design, performance monitoring, and energy efficiency. This article addresses the challenge of long-term missing gaps in dry-bulb temperature data by examining three spatial methods, namely the inverse distance weighting (IDW) method, the spatial regression test (SRT) method, and the substitution with best match data (SSBM) method, as well as two temporal methods, namely the temporal regression test (TRT) method and the temporal substitution with best match data (TSBM) method. Using these methods, missing dry-bulb temperature data with long-term gaps, ranging from 1 to 60 days, are restored for use in building performance monitoring and analysis. Three one-year, hourly datasets were used to evaluate the performance of these approaches. Each method was applied to deal with artificial gaps which were generated randomly and represented different seasons of a year. In terms of the difference between estimated values and measured values, three evaluation indices, namely mean absolute error (MAE), root mean square error (RMSE), and standard error of bias (BIASSTD), were utilized. The comparison results show that spatial methods are better than temporal methods. The confidence level of the SRT method was further investigated by applying this method to existing data and missing data, and examining its performance. The results indicate that the uncertainty of the SRT method can be predicted and at least two neighboring stations are recommended when using it. This is the second part of the research results obtained through the ASHRAE 1413 research project (in press) with a focus on introducing gap-filling methods for long-term gaps in dry-bulb temperature.

Restoration of 1–24 hour dry-bulb temperature gaps for use in building performance monitoring and analysis—Part I

Building energy system retrofit and retro-commissioning projects present tremendous opportunities to save energy. Energy consumption in buildings, especially HVAC systems, is significantly impacted by weather conditions. However, short- or long-term climatic data are frequently missing because of data transmission problems, data quality assurance methods, sensor malfunction, or a host of other reasons. These gaps in climatic data continue to provide challenges for HVAC engineers in monitoring and verifying building energy performance. This article examines eight classical approaches that use Linear interpolation, Lagrange interpolation, and Cubic Spline interpolation techniques, and eleven approaches that use two newly developed methods, i.e., Angle-based interpolation and Corr-based interpolation, to restore up to 24 h of missing dry-bulb temperature data in a time series for use in building performance monitoring and analysis. Eleven one-year hourly data sets are used to evaluate the performance of these 19 different methods. Each method is applied to deal with artificial gaps that are generated randomly. In terms of the difference between estimated values and measured values, two types of comparisons are carried out. The first comparison is conducted with three evaluation indices: MAE, RMSE, and STDBIAS. The second comparison is based on the percentage of the total data that can be estimated by an approach within specific error thresholds, including 1°F (0.56°C), 2°F (1.11°C), 3°F (1.67°C), and 5°F (2.78°C), from measured values. The comparison results show that Linear interpolation performs best when filling 1–2 h gaps, Lagrange interpolation (Lag2L2R) outperforms other methods when gaps are 3–8 h long, and the Corr-based interpolation method (Corr1L1R24Avg) is a better technique for filling 9–24 h gaps. This article presents the first part of the research results through the ASHRAE 1413 research project. The second part of the results focuses on methods to filling long-term dry-bulb temperature gaps.

Challenges in Getting Building Performance Monitoring Tools for Everyday Use: User Experiences with A New Tool

There is a need for building performance monitoring because it is common that buildings do not perform as intended. A number of advanced tools for the purpose have been developed within the last tens of years. However, these tools have not been widely adopted in real use. A new tool presented here utilizes building automation data and transforms the data into a set of performance metrics, and is capable of visualizing building performance from energy, indoor conditions, and HVAC (heating, ventilation and air conditioning) system perspectives. The purpose of this paper is to study the users’ perceptions of the use of tool. The research method was semi-structured interviews. Although the users were satisfied with the solution in general, it was not taken into operative use. The main challenges with the use of the solution were related to accessibility, trust, and management practices. The interviewees were struggling to manage with numerous information systems and therefore had problems in finding the solution and authenticating to it. All the interviewees did not fully trust the solution, since they did not entirely understand what the performance metrics meant or because the solution had limitations in assessing building performance. Management practices are needed to support the performance measurement philosophy.

Characterization and Search of Construction Inspection Plan Spaces Developed Using a Component-Based Planning Approach

Formal construction inspection planning is needed to help reduce the incidence of overlooked or inefficient inspections, and to help realize the potential of emerging sensing technologies. Prior publications by the authors have presented requirements for such an approach and a component-based inspection planning implementation as an approach in addressing some of the requirements towards formal construction inspection planning. Implementation of such a component-based approach for typical construction examples, however, leads to the generation of large, rugged search spaces. This paper first describes the characteristics of search spaces that are generated using a component-based approach to illustrate which search mechanisms are appropriate to explore these search spaces. The paper then describes a set of search algorithms and heuristics that were investigated and evaluated for searching construction inspection plans. Specific discussions on how these algorithms performed in searching within inspection planning spaces are provided based on experiments conducted using a testbed characterizing a construction site and a building performance monitoring decision-making example.