Integrating Smart Ceiling Fans and Communicating Thermostats to Provide Energy-Efficient Comfort

The project goal was to identify and test the integration of smart ceiling fans and communicating thermostats. These highly efficient ceiling fans use as much power as an LED light bulb and have onboard temperature and occupancy sensors for automatic operationbased on space conditions. The Center for the Environment (CBE) at UC Berkeley led the research team including TRC, Association for Energy Affordability (AEA), and Big Ass Fans (BAF). The research team conducted laboratory tests, installed99 ceiling fans and 12 thermostats in four affordable multifamily housing sites in California’s Central Valley, interviewed stakeholders to develop a case study, developed an online design tool and design guide, outlined codes and standards outreach, and published several papers.The project team raised indoor cooling temperature setpoints and used ceiling fans as the first stage of cooling; this sequencing of ceiling fans and air conditioningreducesenergy consumption, especially during peak periods, while providing thermal comfort.The field demonstration resulted in 39% measured compressor energy savings during the April–October cooling seasoncompared to baseline conditions, normalized for floor area. Weather-normalized energy use varied from a 36% increase to 71% savings, withmedian savings of 15%.This variability reflects the diversity in buildings, mechanical systems, prior operation settings, space types, andoccupants’ schedules,preferences, and motivations. All commercial spaces with regular occupancy schedules (and twoof the irregularly-occupied commercial spaces and one of the homes) showed energy savings on an absolute basis before normalizing for warmer intervention temperatures,and 10 of 13 sites showed energy savings on a weather-normalized basis. The ceiling fans provided cooling for one site for months during hot weather when the coolingequipment failed.Occupants reported high satisfaction with the ceiling fans and improved thermal comfort. This technology can apply to new and retrofit residential and commercial buildings

Advancing and demonstrating the Impact Indices method to screen the sensitivity of building energy use to occupant behaviour

A critical gap between the occupant behaviour research field and the building engineering practice limits the integration of occupant-centric strategies into simulation-aided building design and operation. Closing this gap would contribute to the implementation of strategies that improve the occupants’ well-being while reducing the buildings’ environmental footprint. In this view, it is urgent to develop guidelines, standardised methods, and supporting tools that facilitate the integration of advanced occupant behaviour models into the simulation studies. One important step that needs to be fully integrated into the simulation workflow is the identification of influential and non-influential occupant behaviour aspects for a given simulation problem. Accordingly, this article advances and demonstrates the application of the Impact Indices method, a fast and efficient method for screening the potential impact of occupant behaviour on the heating and cooling demand. Specifically, the method now allows the calculation of Impact Indices quantifying the sensitivity of building energy use to occupancy, lighting use, plug-load appliances use, and blind operation at any spatial and temporal resolution. Hence, users can apply it in more detailed heating and cooling scenarios without losing information. Furthermore, they can identify which components in building design and operation require more sophisticated occupant behaviour models. An office building is used as a real case study to illustrate the application of the method and asses its performance against a one-factor-at-a-time sensitivity analysis. The Impact Indices method indicates that occupancy, lighting use and plug-load appliances have the greatest impact on the annual cooling demand of the studied office building; blind operation is influential only in the west and south façades of the building. Finally, potential applications of the method in building design and operation practice are discussed

Contributions of heat pumps to demand response: A case study of a plus-energy dwelling

- Premio ETSI al mejor artículo científico del Trimestre. Junio 2018. - Artículo subido a idUS con permiso de su primera autora (Laura Romero Rodríguez), que proporciona las versiones preprint y postprint.Demand Response programs are increasingly used in the electricity sector, since they allow consumers to play a significant role for balancing supply and demand by reducing or shifting their electricity consumption. For that purpose, incentives such as time-based rates have been proposed. The present study analyzes the potential benefits of operating the heat pump of a plus-energy dwelling which participates in a dynamic pricing market, benefitting from the thermal storage capacity of the building. The software TRNSYS 17 has been used to model the building and the supply system. A validation of the model was carried out by using available measurements of the dwelling. Three setpoint temperature scenarios have been considered for sixteen different strategies which depend on temperature and electricity price thresholds, with the aim of determining which alternatives could lead to significant savings while maintaining an acceptable thermal comfort. Several factors such as cost savings, heat pump consumption, ratio of self-consumption of the dwelling and use of the heat pump during peak hours were also evaluated in every case. The results show that dynamic price thresholds should be used instead of fixed price thresholds, which may cause low activations of the heat pump or overheat the building above the comfort limits. Cost savings up to 25% may be achieved by using optimal strategies, increasing the self-consumption ratio, having almost no influence on the thermal comfort and achieving significant peak reductions on the grid. The outcomes of this study show the importance of looking at the implications of such strategies on several criteria within a demand response framework.Ministerio de Economía y CompetitividadUniversidad de Sevilla. V Plan Propio de Investigación (VPPI-US)Unión Europea. Horizon2020. Grant agreement No. 69596

INTEGRATED MODELING AND MONITORING FOR A HEALTHY AND SUSTAINABLE BUILDING ENVIRONMENT

The transmission of airborne diseases indoors is a significant challenge to public health. Buildings are hotspots for viral transmission, which can result in adverse effects on human health and quality of life, especially considering that individuals spend approximately 87% of their time indoors. The emergence of the COVID-19 pandemic has highlighted the importance of considering health aspects during the development of sustainable built environments. Consequently, maintaining a healthy, sustainable, and comfortable built environment represents a major challenge for facilities management teams. However, research on the infection risks associated with emerging pandemics is still in its infancy, and the effectiveness of intervention strategies remains uncertain. Furthermore, the complex interplay between health, energy consumption, and human comfort remains poorly understood, impeding the development of comprehensive control strategies that encompass all three critical dimensions of building sustainability. In addition, existing technologies have limitations to conduct real-time monitoring, while current communication methods between occupants and facilities management teams suffer from a lack of effectiveness, user-friendliness, and informativeness. These deficiencies hinder their ability to address the pressing needs of occupants during pandemics. To address these challenges, this dissertation proposes a convergent framework that integrates modeling, simulation, and monitoring methodologies for the development and maintenance of a sustainable built environment. Airborne transmission risks were first modeled and estimated under different epidemic scenarios, allowing for the evaluation of various intervention strategies. Facility data was then used to develop methods for modeling and simulating the dimensions of energy consumption and thermal comfort, allowing for the identification of tradeoff relationships among health, energy, and comfort, and quantitatively analyzing the impact of indoor environments through HVAC control strategies on the three major dimensions. Finally, an integrated platform was developed to enable the real-time assessment of health, energy, and comfort, including monitoring, visualization, and conversational communication functionalities. The developed framework thus encompasses modeling, simulation, monitoring, and communication capabilities and can be widely adopted by facility management teams, providing insights and guidance to governments and policymakers based on their specific needs. The applicability of the framework extends beyond specific pandemics and can be used to address a broader range of infectious diseases

Impact of window-to-wall ratio on heating demand and thermal comfort when considering a variety of occupant behavior profiles

Energy consumption and thermal comfort in residential buildings are highly influenced by occupant behavior, which exhibits a high level of day-to-day and dwelling-to-dwelling variance. Although occupant behavior stochastic models have been developed in the past, the analysis or selection of a building design parameter is typically based on simulations that use a single “average” occupant behavior schedule which does not account for all possible profiles. The objective of this study is to enhance the understanding of how window-to-wall ratio (WWR) of a residential unit affects heating demand and thermal comfort when considering occupant behavior diversity through a parametric analysis. To do so, a stochastic occupant behavior model generates a high number of possible profiles, which are then used as input in an energy simulation of the dwelling. As a result, one obtains probability distributions of energy consumption and comfort for different WWR values. The paper shows that the shape of the probability distributions is affected by WWR and dwelling orientation, and that the influence of different occupant behavior aspects on performance also varies with WWR. This work could help designers to better assess the impact of WWR for a large spectrum of possible occupant behavior profiles

Non-invasive detection algorithm of thermal comfort based on computer vision

The waste of building energy consumption is a major challenge in the world. Real-time detection of human thermal comfort is an effective way to deal with this issue. However, due to the difference of personal thermal comfort and changes caused by climatic variations, there is still a long way to reach this target. From another perspective, the current HVAC (heating, ventilating and air-conditioning) systems are reluctant to provide flexible interaction channels to adjust atmosphere which fails to follow continuously increasing requirements from users. All of them indicate the necessity to develop more intelligent detection method for human thermal comfort. In this paper, a non-invasion detection method toward thermal comfort is proposed from two perspectives: macro human postures and skin textures. In posture part, OpenPose is used for detecting the key points’ position coordinates of human body in images, which would be functionalized from the term of thermal comfort. In skin textures, deep neural network is used to regress the images of skin to its temperature. Based on Fanger’s theory of thermal comfort, the results of both parts are satisfying: subjects’ postures can be captured and interpreted into different thermal comfort level: hot, cold and comfort. And the absolute error of prediction from neurons network is less than 0.125 degrees centigrade which is the equipment error of thermometer used in data acquisition. With solutions of this paper, it is promising to non-invasively detect the thermal comfort level of users from postures and skin textures. And the conclusion and future work are discussed in final chapter

Learning and Control using Gaussian Processes

Building physics-based models of complex physical systems like buildings and chemical plants is extremely cost and time prohibitive for applications such as real-time optimal control, production planning and supply chain logistics. Machine learning algorithms can reduce this cost and time complexity, and are, consequently, more scalable for large-scale physical systems. However, there are many practical challenges that must be addressed before employing machine learning for closed-loop control. This paper proposes the use of Gaussian Processes (GP) for learning control-oriented models: (1) We develop methods for the optimal experiment design (OED) of functional tests to learn models of a physical system, subject to stringent operational constraints and limited availability of the system. Using a Bayesian approach with GP, our methods seek to select the most informative data for optimally updating an existing model. (2) We also show that black-box GP models can be used for receding horizon optimal control with probabilistic guarantees on constraint satisfaction through chance constraints. (3) We further propose an online method for continuously improving the GP model in closed-loop with a real-time controller. Our methods are demonstrated and validated in a case study of building energy control and Demand Response