Buildings account for about 40% of the global energy consumption. Renewable energy resources are one possibility to mitigate the dependence of residential buildings on the electrical grid. However, their integration into the existing grid infrastructure must be done carefully to avoid instability, and guarantee availability and security of supply. Demand response, or demand-side management, improves grid stability by increasing demand flexibility, and shifts peak demand towards periods of peak renewable energy generation by providing consumers with economic incentives. This paper reviews the use of reinforcement learning, a machine learning algorithm, for demand response applications in the smart grid. Reinforcement learning has been utilized to control diverse energy systems such as electric vehicles, heating ventilation and air conditioning (HVAC) systems, smart appliances, or batteries. The future of demand response greatly depends on its ability to prevent consumer discomfort and integrate human feedback into the control loop. Reinforcement learning is a potentially model-free algorithm that can adapt to its environment, as well as to human preferences by directly integrating user feedback into its control logic. Our review shows that, although many papers consider human comfort and satisfaction, most of them focus on single-agent systems with demand-independent electricity prices and a stationary environment. However, when electricity prices are modelled as demand-dependent variables, there is a risk of shifting the peak demand rather than shaving it. We identify a need to further explore reinforcement learning to coordinate multi-agent systems that can participate in demand response programs under demand-dependent electricity prices. Finally, we discuss directions for future research, e.g., quantifying how RL could adapt to changing urban conditions such as building refurbishment and urban or population growth.
John H. Zimmerman, Alan Williams, Brian Schumacher
et al.
Subsurface contamination can migrate upward into overlying buildings, exposing the buildings’ inhabitants to contaminants that can cause detrimental health effects. This phenomenon is known as vapor intrusion (VI). When evaluating a building for VI, one must understand that seasonal and short-term variability are significant factors in determining the reasonable maximum exposure (RME) to the occupants. RME is a semi-quantitative term that refers to the lower portion of the high end of the exposure distribution—conceptually, above the 90th percentile exposure but less than the 98th percentile exposure. Samples were collected between December 2020 and April 2022 at six non-residential commercial buildings in Fairbanks, Alaska. The types of samples collected included indoor air (IA); outdoor air; subslab soil gas; soil gas; indoor radon; differential pressure; indoor and outdoor temperature; heating, ventilation, and air conditioning (HVAC) parameters; and other environmental factors. The buildings in close proximity to the volatile organic compound (VOC) source/release points presented less variability in indoor air concentrations of trichloroethylene (TCE) and tetrachloroethylene (PCE) compared to the buildings farther down gradient in the contaminated groundwater plume. The VOC data pattern for the source area buildings shows an outdoor air temperature-dominated behavior for indoor air concentrations in the summer season. HVAC system operations had less influence on long-term indoor air concentration trends than environmental factors, which is supported by similar indoor air concentration patterns independent of location within the plume. The use of soil temperature and indoor/outdoor temperatures as indicators and tracers (I&Ts) across the plume as predictors of the sampling period could produce a good estimation of the RME for the building occupants. These results, which show the use of soil temperature and indoor/outdoor temperatures as I&Ts, will help advance investigative methods for evaluation of VI in similar settings and thereby improve the protection of human health in indoor environments.
Energy use is second to staffing in building operating costs. Sustainable technology in the energy sector is based on utilizing renewable sources of energy such as solar, wind, glazing systems, insulation. Other areas of focus include heating, ventilation and air conditioning; novel materials and construction methods; improved sensors and monitoring systems; and advanced simulation tools that can help building designers make more energy efficient choices. The objective of this research is studying the effect of insulations on energy consumption of buildings in Iraq and identifying the amount of energy savings from application the insulations in buildings. HAP (Hourly Analysis Program) is used to calculate the thermal loads and the amount of energy needed. It is concluded that the use of the thermal insulation in the roof, walls, floors, and double glazing system for windows in building effectively reduces the energy required for air conditioning.
Background While the range of possible transmission pathways of severe acute respiratory syndrome coronavirus-2 in various settings has been investigated thoroughly, most authorities have recently acknowledged the role of aerosol spread in its transmission, especially in indoor environments where ventilation is poor. Engineering controls are needed to mitigate aerosol transmission in high-risk settings including hospital wards, classrooms and offices. Aim To assess the effectiveness of aerosol filtration by portable air cleaning devices with high-efficiency particulate air filters used in addition to a standard building heating ventilation and air conditioning (HVAC) system. Methods Test rooms, including a single-bed hospital room, were filled with test aerosol to simulate aerosol movement. Aerosol counts were measured over time with various portable air cleaning devices and room ventilation systems to quantify the overall aerosol clearance rate. Findings Portable air cleaning devices were very effective for removal of aerosols. The aerosols were cleared five times faster in a small control room with portable air cleaning devices than in the room with HVAC alone. The single-bed hospital room had an excellent ventilation rate (∼14 air changes per hour) and cleared the aerosols in 20 min. However, with the addition of two air cleaning devices, the clearance time was three times faster. Conclusions Inexpensive portable air cleaning devices should be considered for small and enclosed spaces in healthcare settings, such as inpatient rooms and personal protective equipment donning/doffing stations. Portable air cleaning devices are particularly important where there is limited ability to reduce aerosol transmission with building HVAC ventilation.
Abstract Air handling units are key sub-systems of heating, ventilation and air conditioning systems, which are used to condition air to satisfy human comfort requirements. Fault prognosis allows maintenance crews to identify the Remaining Useful Life (RUL) of a system, thus unexpected breakdowns are avoided, leading to a decrease in maintenance costs. To estimate RULs, a Hidden Semi-Markov Model (HSMM)-based method is proposed. To estimate states of HSMMs accurately, a revised scaled method is developed to guarantee that state estimates do not approximate to infinity. Additionally, a new discrete statistical process control method is developed to filter out false state estimates of HSMMs. To estimate RULs of components and systems accurately and effectively, a backward recursive method is developed to integrate HSMMs’ parameters of time-duration distributions for multiple failure modes to generate those of components and systems directly, thus low computational effort is achieved. Experimental results illustrate that the RULs of components/systems can be predicted by our method accurately in an efficient way.
Abstract Heating ventilation and Air Conditioning (HVAC) systems consume a substantial volume of energy within corporate buildings, mainly due to lack of severe monitoring which results in compromising either energy efficiency or user comfort. We propose a simple HVAC control system that automates the HVAC operation in real time by considering energy management policies and user preferences. Our system is built on top of IoT (Internet of Things) framework, where appliances in a laboratory are automated with suitable sensors also thermal parameters are obtained from sensors and user feedback information are collected for real-time processing in our distributed cloud environment. We utilized Blynk Application Programming Interface (API) to monitor the real time data from sensors, to obtain user feedback periodically and to dynamically adjust the temperature settings based on energy management policies, user feedback and sensor values. The effectiveness of HVAC is evaluated mathematically. Our experiments indicate that we achieve total saving of 0.9 kWhr and also maintain user thermal comfort consistently.
Abstract Energy consumption of building takes a big proportion among all consumers, which promotes the research topics around building energy saving very hot. However, it is very hard and impossible to introduce all current trends in such a big field in an article. Nearly all branches and small crossing fields have been reviewed in the open literature. Considering the two aforementioned reasons, the research trends in building and energy saving are shortly reviewed here, taking the topic of air source heat pump as a typical example. Firstly, the background of building energy saving and its highlights over the past 50 years are divided and introduced, supported with published data of journal articles in Elsevier. It is expected to show the reader a whole map about this topic. This is followed by the reviews on building materials, consisting of thermal insulation material and phase change materials, and on building equipment, from building automation system to heating, ventilation and air-conditioning system, and then to detailed air parameter control strategy. Thirdly, it is reached to the air source heat pump, with trends around system and frosting/defrosting problem separately reviewed. As concluded, both of fundamental mechanism researches and application technology solutions are important. Research trends in this field are moving towards intelligent, multidimensional, and interdisciplinary.
This study aims at introducing a modeling and simulation approach for a green roof system which can reduce energy cost of a building exposed to high temperatures throughout the summer season. First, to understand thermal impact of a green roof system on a building surface, a field-based study has been conducted in Commerce, Texas, U.S., where the average maximum temperature in summer is 104 °F (40 °C). Two types of analyses were conducted: (1) comparison of temperature between different plant type via Analysis of variance (ANOVA) and (2) polynomial regression analysis to develop thermal impact estimation model based on air temperature and presence of a green roof. In addition, an agent-based simulation (ABS) model was developed via AnyLogic<sup>®</sup> University 8.6.0 simulation software, Chicago, IL, U.S., in order to accurately estimate energy cost and benefits of a building with a photovoltaic-green roof system. The proposed approach was applied to estimate energy reduction cost of the Keith D. McFarland Science Building at Texas A&M University, Commerce, Texas (33.2410° N, 95.9104° W). As a result, the proposed approach was able to save $740,325.44 in energy cost of a heating, ventilation, and air conditioning (HAVC) system in the subject building. The proposed approach will contribute to the implementation of a sustainable building and urban agriculture.
The ejector is one of the key components of a transcritical CO2 ejector expansion refrigeration system, and the nozzle exit position (NXP) is the main geometric parameter. Therefore, research on the structure, performance, and internal flow mechanism of the ejector is beneficial for improving the performance of the ejector system. In this study, a visualization ejector with different NXPs was designed and manufactured. The entrainment ratio and pressure recovery ratio of the CO2 two-phase ejector with different NXPs were obtained in a transcritical CO2 ejector expansion refrigeration system. Furthermore, the pressure distribution along the primary nozzle, mixing section, and diffuser section was measured using a pressure sensor. Simultaneously, the expansion angle, expansion length, and phase change position of the primary flow were captured using a high-speed camera. According to the experimental results, the performance of the ejector is influenced by the expansion profile. Specifically, the short NXP (4 mm) led to a short expansion length, large expansion angle, and high-pressure recovery ratio, but the corresponding entrainment ratio was relatively low. As the NXP increased to 6 mm and 8 mm, the expansion of the primary flow was more sufficient, the expansion length was longer, the expansion angle was smaller, and the entrainment ratio was improved. A further increase in the nozzle distance to 10 mm contributed to a larger expansion angle as well as a low entrainment ratio and pressure recovery ratio. From the results, the optimal NXP is in the range of 6–8 mm for the ejector under the operating conditions of this study. The results of this study contribute to a better understanding of the internal flow mechanism of the ejector and help to improve its design theory.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
To intelligently schedule heating, ventilation, and air conditioning (HVAC) systems for reducing energy cost of commercial buildings, indoor temperature and indoor air quality (IAQ) should be jointly considered. Otherwise, the health and productivity of occupants may be affected. In this paper, we investigate the problem of minimizing the sum of energy cost associated with HVAC systems and thermal discomfort cost related to occupants in multi-zone commercial buildings considering IAQ management. Firstly, by taking uncertainties of electricity price, outdoor temperature, number of occupants, temperature preference of each occupant, and external thermal disturbance into consideration, we formulate a time-averaged expected total cost minimization problem without violating the constraints of indoor temperature and IAQ. Due to the existence of uncertain system parameters, temporally and spatially coupled constraints, the nonconvex objective function, and nonconvex constraints, it is particularly challenging to solve the formulated problem. To this end, we propose a real-time algorithm based on the framework of Lyapunov optimization techniques. The key idea of the proposed algorithm is to construct virtual queues related to indoor temperatures and stabilize such queues so that indoor temperatures fluctuate around the ideal time-average indoor temperature. By dynamically controlling the average fluctuation level of indoor temperatures, the total cost could be optimized. Extensive simulation results show the effectiveness of the proposed algorithm.
Abstract Solar-reflective “cool” walls reduce absorption of sunlight by the building envelope, which may decrease cooling load in warm weather and increase heating load in cool weather. Changes to annual heating, ventilation, and air conditioning (HVAC) energy use depend on climate, wall construction, wall orientation, building geometry, HVAC efficiency, and operating schedule. Changes to annual energy cost and energy-related emissions further vary with local energy prices and emission factors. We used EnergyPlus to perform over 100,000 whole-building energy simulations, spanning 10 different building categories, three building vintages, 16 California climate zones, and 15 United States (U.S.) climate zones. Cool walls yielded annual source energy, energy cost, and emission savings in all California climate zones and in warm U.S. (ASHRAE) climate zones. In California, annual whole-building HVAC energy cost savings were 4.0–27% in single-family homes, 0.5–3.8% in medium office buildings, and 0.0–8.5% in stand-alone retail stores. In warm U.S. climates—zones 1A (Miami, FL) through 4B (Albuquerque, NM)—annual HVAC energy cost savings were 1.8–8.3% in single-family homes, 0.3–4.6% in medium office buildings, and 0.5–11% in stand-alone retail stores. California and U.S. fractional source energy and emission savings were comparable to fractional energy cost savings. Per unit surface area modified, cool-wall savings often exceeded cool-roof savings because building codes typically prescribe much less wall insulation than roof insulation.
Zhang Chaoshi, Huang Zhanfeng, Huang Chaiyi
et al.
To measure the vapor-liquid equilibrium (VLE) of refrigerants/lubricants, a refrigerants/lubricants VLE experimental system with high precision was developed based on the cycle method. The circulation system was equipped with a detachable sample tank. It combined the cycle method and weighing method and eliminated the systematic error of isochoric saturation method due to the assumption. Furthermore, it could measure the VLE of refrigerants/lubricants at a temperature range of 263.15 K to 373.15K. The saturated vapor pressure of R290 was measured from 293.35 K to 323.33 K. The relative deviation of the pressure was less than -0.18% when compared to NIST REFPROP9.1. Additionally, the phase equilibrium values of R290 and squalane were measured at 303.35 K. The experimental data and literature data were correlated with the Peng–Robinson (PR) equation of state and one-fluid van der Waals mixing rules in Aspen Plus, and the results indicated maximum relative deviation of pressure of-0.37% and0.76%, respectively.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration