Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part II Field Evaluation

In a heating, ventilation and air conditioning (HVAC) system, a whole building fault (WBF) refers to a fault that occurs in one component but may trigger additional faults/abnormalities on different components or subsystems resulting in impacts on the energy consumption or indoor air quality in buildings. At the whole building level, interval data collected from various components/subsystems can be employed to detect WBFs. In the Part I of this study, a novel data-driven method which includes weather and schedule-based Pattern Matching (WPM) procedure and a feature based principal component analysis PCA (FPCA) procedure was developed to detect the WBF. This article is the second of a two-part study of the development of the whole building fault detection method. In the Part II of the study (this paper), various WBFs were designed and imposed in the HVAC system of a campus building. Data from both imposed fault and naturally-occurred faults were collected through the Building Automation System to evaluate the developed fault detection method. Evaluation results show that the developed WPM-FPCA method reaches a high detection rate and a low false alarm rate.

A Statistical Evaluation of Combining Human Productivity Metrics in the Indoor Environment

The potential of improving human productivity by providing healthy indoor environments has been a consistent interest in the building field for decades. This research field’s long-standing challenge is to measure human productivity given the complex nature of office work. Previous studies have diversified productivity metrics, allowing greater flexibility in collecting human data; however, this diversity complicates the ability to combine productivity metrics from disparate studies within a meta-analysis. This study aims to categorize existing productivity metrics and statistically assess which categories show similar behavior when used to measure the impacts of indoor environmental quality. The 106 productivity metrics compiled were grouped into six productivity metric categories: neurobehavioral speed, accuracy, neurobehavioral response time, call handling time, self-reported productivity, and performance score. Then, this study set neurobehavioral speed as the baseline category given its fitness to the efficiency-based definition of productivity (i.e., output versus input) and conducted three statistical analyses with the other categories to evaluate their similarity. As results, the categories of neurobehavioral response time, self-reported productivity, and call handling time were found to have statistical similarity with neurobehavioral speed. This study contributes to creating a constructive research environment for future meta-analyses to understand which human productivity metrics can be combined with each other.

Well-being in the Built Environment

This editorial provides the background of the special edition. Current understanding of how a built environment, especially an indoor environment, affects human health and wellbeing is briefly summarized. Several recent “Ten Questions” papers on this topic are reviewed. Needs and challenges regarding this topic are discussed.

Associations Among Home Indoor Environmental Quality Factors and Worker Health while Working from Home during COVID-19 Pandemic

The outbreak of SARS-CoV-2 virus forced office workers to conduct their daily work activities from home over an extended period. Given this unique situation, an opportunity emerged to study the satisfaction of office workers with indoor environmental quality (IEQ) factors of their houses where work activities took place and associate these factors with mental and physical health. We designed and administered a questionnaire that was open for 45 days during the COVID-19 pandemic and received valid data from 988 respondents. The results show that low satisfaction with natural lighting, glare and humidity predicted eye related symptoms, while low satisfaction with noise was a strong predictor of fatigue or tiredness, headaches or migraines, anxiety, and depression or sadness. Nose and throat related symptoms and skin related symptoms were only uniquely predicted by low satisfaction with humidity. Low satisfaction with glare uniquely predicted an increase in musculoskeletal discomfort. Symptoms related to mental stress, rumination or worry were predicted by low satisfaction with air quality and noise. Finally, low satisfaction with noise and indoor temperature predicted the prevalence of symptoms related to trouble concentrating, maintaining attention or focus. Workers with higher income were more satisfied with humidity, air quality and indoor temperature and had better overall mental health. Older individuals had increased satisfaction with natural lighting, humidity, air quality, noise, and indoor temperature. Findings from this study can inform future design practices that focus on hybrid home-work environments by highlighting the impact of IEQ factors on occupant well-being.

Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part I Development of the Method

A whole building fault (WBF) refers to a fault occurring in one component, but may cause impacts on other components or subsystems, or arise impacts of significant energy consumption and thermal comfort. Conventional methods which targeted at the component level fault detection cannot be successfully employed to detect a WBF because of the fault propagation among the closely coupled equipment or subsystems. Therefore, a novel data-driven method named weather and schedule-based pattern matching (WPM) and feature based principal component analysis (FPCA) method for WBF detection is developed. Three processes are established in the WPM-FPCA method to address three main issues in the WBF detection. First, a feature selection process is used to pre-select data measurements which represent a whole building's operation performance under a satisfied status, namely baseline status. Secondly, a WPM process is employed to locate weather and schedule patterns in the historical baseline database, that are similar to that from the current/incoming operation data, and to generate a WPM baseline. Lastly, PCA models are generated for both the WPM baseline data and the current operation data. Statistic thresholds used to differentiate normal and abnormal (faulty) operations are automatically generated in this PCA modeling process. The PCA models and thresholds are used to detect WBF. This paper is the first of a two-part study. Performance evaluation of the developed method is conducted using data collected from a real campus building and will be described in the second part of this paper.

How Work From Home Has Affected the Occupant's Well-Being in the Residential Built Environment: An International Survey Amid the COVID-19 Pandemic

This paper presents the results from an international survey that investigated the impacts of the built environment on occupant well-being during the COVID-19 pandemic when most professionals were forced to work from home (WFH). The survey was comprised of 81 questions focusing on the respondent's profiles, residences, home indoor environmental quality, health, and home working experiences. A total of 1,460 responses were collected from 35 countries, and 1,137 of them were considered complete for the analysis. The results suggest that home spatial layout has a significant impact on occupant well-being during WFH since home-life distractions and noises due to the lack of a personal workspace are likely to prevent productive work. Lack of scenic views, inadequate daylighting, and poor acoustics were also reported to be detrimental to occupant productivity and the general WFH experience. It is also revealed from this survey that temperature, relative humidity, and indoor air quality generally have higher satisfaction ratios compared with the indoor lighting and acoustic conditions, and the home layout. Hence, home design for lighting, acoustics, and layout should also receive greater attention in the future

Energy demand reduction in the built environment using shallow geothermal integrated energy systems: Part II – Hybrid ground source heat pump for building heating

The research collection aims at finding the various possible opportunities for the effective integration of shallow geothermal energy (SGE) to decrease the energy demand in the built environment and to reduce emission associated with it. The direct utilization of SGE using a ground source heat pump (GSHP) has been reviewed in comprehensive review part I and part II. From the extensive review, it is found that the hybrid GSHP is needed to avoid ground thermal imbalance and peak demand. Hybrid GSHP can adopt various supplemental heat sources and sinks according to the local climatic conditions and the balance of energy demands. The primary focus on the integration of subsystems such as biomass, solar energy (PV, PVT, and collector), phase change material, micro gas turbine, and absorption heat pump with GSHP is presented for heating application. This comprehensive review part III highlights the recent research findings and a potential gap in hybrid GSHP for further research and developments

Energy demand reduction in the built environment using shallow geothermal integrated energy systems – A comprehensive review: Part I. Design consideration of ground heat exchanger

The research collection aims at finding the various possible opportunities for the effective integration of shallow geothermal energy (SGE) to decrease the energy demand in the built environment and to reduce emission associated with it. The integration of SGE with heat pump using pipe network is extensively reviewed. The open loop and closed loop (vertical, horizontal, energy piles) pipe networks are the most common type of ground heat exchanging methods. The objective of the review is to improve the heat exchanger effectiveness through various design aspects according to the local climatic conditions. This comprehensive review part II contains the research details pertaining to the last two decades about ground heat exchangers (geometrical aspects, borehole material, grout material, thermal response test, analytical and numerical models). Also, the factors influencing the ground heat exchanger's performance such as heat transfer fluid, groundwater flow, and soil properties are discussed in detail. This paper highlights the recent research findings and a potential gap in the ground heat exchanger.

Potential of Utilizing Thermal Energy Storage Integrated Ground Source Heat Pump System to Reshape Electricity Demand in the United States

More than 20% of electricity generated in the United States each year is consumed for meeting the thermal demands (e.g., space cooling, space heating, and water heating) in residential and commercial buildings. Integrating thermal energy storage (TES) with building's HVAC systems has the potential to reshape the electric load profile of the building and mitigate the mismatch between the renewable generation and the demand of buildings. A novel ground source heat pump (GSHP) system integrated with underground thermal energy storage (UTES) has been proposed to level the electric demand of buildings while still satisfying their thermal demands. This study assessed the potential impacts of the proposed system with a bottom-up approach. The impacts on the electricity demand in various electricity markets were quantified. The results show that, within the capacity of the existing electric grids, the maximum penetration rate of the proposed system in different wholesale markets could range from 51% to 100%. Overall, about 46 million single-family detached houses can be retrofitted into the proposed system without increasing the annual peak demand of the corresponding markets. By implementing the proposed system at its maximum penetration rate, the grid-level summer peak demand can be reduced by 9.1% to 18.2%. Meanwhile, the annual electricity consumption would change by −12 % to 2%. The nationwide total electricity consumption would be reduced by 9%.

An Investigation of the Impact of COVID-19 Pandemic on Energy Consumption in the United States

Lockdown measures and mobility restrictions to combat the spread of COVID-19 have impacted energy consumption patterns. The overall decline of energy use during lockdown restrictions can best be identified through the analysis of energy consumption by source and end-use sectors. Using monthly energy consumption data, the total 9-months use between January and September for the years 2015–2020 is calculated for each end-use sector (transportation, industrial, residential, and commercial). The cumulative consumption within these 9 months of the petroleum, natural gas, biomass, and electricity energy by the various end-use sectors are compared. The analysis shows that the transportation sector experienced the greatest decline (14.38%). To further analyze the impact of COVID-19 on each state within the USA, the consumption of electricity by each state and each end-use sector in the times before and during the pandemic is used to identify the impact of specific lockdown procedures on energy use. The distinction of state-by-state analysis in this study provides a unique metric for consumption forecasting. The average total consumption for each state was found for the years 2015–2019. The total average annual growth rate (AAGR) for 2020 was used to find a correlation coefficient between COVID-19 case and death rate, population density, and lockdown duration. A correlation coefficient was also calculated between the 2020 AAGR for all sectors and AAGR for each individual end-user. The results show that Indiana had the highest percent reduction in consumption of 10.07% while North Dakota had the highest consumption increase of 7.61%. This is likely due to the amount of industrial consumption relative to other sectors in the state.