More Like this

1. Spatiotemporal Variations in Ozone and Carbon Dioxide Concentrations in an HVAC System of a LEED-Certified Office Building

   https://doi.org/10.1016/j.buildenv.2025.112651  

   Jiang, Jinglin; Huang, Junkai; Jung, Nusrat; Boor, Brandon E (April 2025, Building and Environment)

   Indoor air quality (IAQ) is crucial for the health, well-being, and productivity of office occupants. IAQ is strongly influenced by occupancy and the operational mode of the heating, ventilation, and air conditioning (HVAC) system. This study investigates the spatiotemporal variations in ozone (O3) and carbon dioxide (CO2) concentrations throughout the HVAC system of a LEED-certified office building. A four-month field measurement campaign was conducted at the Ray W. Herrick Laboratories, employing an automated multi-point sampling system to monitor O3 and CO2 at eight locations throughout the HVAC system. The objectives of this study are to characterize the spatiotemporal distribution of these gases under different ventilation modes and occupancy levels, and to identify O3 loss mechanisms in the office and its HVAC system. Spatiotemporal variations in O3 and CO2 concentrations were observed throughout the HVAC system. Results indicate that outdoor air exchange rates (AERs) significantly impact indoor O3 levels, with higher AERs resulting in increased indoor O3 but reduced CO2 concentrations. Measurements reveal that HVAC filters and ducts contribute to O3 loss, with up to 18% O3 removal observed in the longest HVAC duct segment. Additionally, occupancy influences O3 deposition onto human skin and clothing surfaces. This research underscores the limitations of ventilation standards that focus only on CO2, highlighting the need for ventilation strategies that consider the effects of occupancy and outdoor AERs on different gases. By integrating multi-point gas sampling into building automation systems, more effective control strategies can be developed to enhance IAQ and occupant health while reducing energy consumption. 

   more » « less
2. Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations

   Mohammadshirazi, Ahmad; Nadafian, Aida; Monsefi, Amin Karimi; Rafiei, Mohammad; Ramnath, Rajiv (August 2023, https://doi.org/10.48550/arXiv.2308.01438)

   Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quality "ahead-of-time" approximations. Undoubtedly, accurate indoor air quality approximation significantly helps provide a healthy indoor environment, optimize associated energy consumption, and offer human comfort. However, it is crucial to design an ML architecture to capture the domain knowledge, so-called problem physics. In this study, we propose six novel physics-based ML models for accurate indoor pollutant concentration approximations. The proposed models include an adroit combination of state-space concepts in physics, Gated Recurrent Units, and Decomposition techniques. The proposed models were illustrated using data collected from five offices in a commercial building in California. The proposed models are shown to be less complex, computationally more efficient, and more accurate than similar state-of-the-art transformer-based models. The superiority of the proposed models is due to their relatively light architecture (computational efficiency) and, more importantly, their ability to capture the underlying highly nonlinear patterns embedded in the often contaminated sensor-collected indoor air quality temporal data. 

   more » « less
3. A Tree-Structured Neural Network Model for Household Energy Breakdown

   https://doi.org/10.1145/3308558.3313405  

   Jia, Yiling; Batra, Nipun; Wang, Hongning; Whitehouse, Kamin (May 2019, The World Wide Web Conference)

   Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances. 

   more » « less
4. Learning from Non-Experts: An Interactive and Adaptive Learning Approach for Appliance Recognition in Smart Homes

   https://doi.org/10.1145/3491241  

   Codispoti, Jackson; Khamesi, Atieh R.; Penn, Nelson; Silvestri, Simone; Shin, Eura (February 2022, ACM Transactions on Cyber-Physical Systems)

   With the acceleration of ICT technologies and the Internet of Things (IoT) paradigm, smart residential environments , also known as smart homes are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems, and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition . Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert , always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this paper, we develop a SAL algorithm, called K -Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness , representativeness , and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency. 

   more » « less
5. Numerical Assessment of Indoor Air Quality in Spaces in the United States Designed with the ASHRAE 62.1–2019 Natural Ventilation Procedure

   Sas-Wright, Troy; Clark, Jordan D (August 2023, Building and Environment)

   Natural ventilation is used to cool buildings and cut energy costs by inducing airflow through building openings without the use of mechanical ventilation and cooling systems. However, prior research has documented increased introduction of particles into indoor environments that are naturally ventilated, with associated health consequences. The recently updated ASHRAE Standard 62.1–2019: Ventilation for Acceptable Indoor Air Quality Natural Ventilation Procedure (NVP) prescribes opening sizes as a function of occupant density and geometry for use as a ventilation strategy, a change from the previous standard. The current work quantifies the indoor air quality impacts of implementing the Standard 62.1–2019 Natural Ventilation Procedure in the United States and compares it to the 62.1-specified ventilation rate procedure. This is done via coupled transient simulation of CONTAM 3.4 and EnergyPlus 9.1. Three pollutant classes were identified to represent a broad range of contaminants: outdoor-generated pollutants, pollutants generated indoors by humans, and pollutants generated indoors by the building itself. With boundary conditions from measured weather and outdoor pollutant data for 13 representative locations throughout the U.S., our modeling first found 41%–185% annual average increase in ventilation rates over its mechanical counterpart if the NVP is used across the geometries and occupant densities in the Standard. Due to elevated ventilation rates, the Natural Ventilation Procedure reduced building-generated and occupant-generated contaminant concentrations during occupied hours by an average of 17%–61% compared to the ventilation rate procedure. Outdoor-generated fine particles averaged 2.1–2.5 times the concentrations indoors when using the NVP as compared to mechanical ventilation with a MERV-8 filter and 7.8–10.4 times the concentration of a mechanical system with a MERV-13 filter. Both ventilation rates and concentrations were substantially climate-specific and somewhat window geometry-specific. We further showed that increased filtration is needed in many cases to keep up with increased effective NVP rates in the 2019 Standard if acceptable levels of indoor particles are to be achieved, and we offer suggestions for improving the Standard. 

   more » « less