Thermal Set Points Research Articles

Neural-assisted HVACs optimal scheduling for renewable energy communities

Renewable energy communities (RECs) show a high potential for the efficient use of distributed energy technologies at local levels according to the Clean Energy Package of the European Union. Nevertheless, many problems are encountered during the planning phase of a REC because a large number of decision variables should be considered depending on the types of community participants and their load profiles. Moreover, the load schedule is fundamental with the aim of maximizing the energy shared within the community and the economic revenue of the REC. This is the main objective of this work that deals with the problem of optimal scheduling for electrical heating and cooling systems in a university lecture building included in a renewable energy community. The conditioning systems are significant electrical loads that can be scheduled both to reduce the energy requirements of the buildings and to synchronize energy injection and withdrawal from the grid, achieving energy self-consumption. The energy demand for the building is estimated using a specific simulation environment (EnergyPlus) which is experimentally validated with a campaign of measurements. From this software, a training dataset is created and used to implement a computationally lean neural network to represent through a black-box model the thermal-electrical behavior of the lecture building. Through the use of this neural model, the building thermal set points are processed by an optimization algorithm to maximize the economic revenue of the REC, while retaining the comfort boundaries of the building. The innovative proposed strategy is applied on a yearly simulation of the REC resulting in reduced energy required for thermal conditioning, higher cash-flows associated with the energy market and a smaller carbon footprint of the community.

A Generative Deep Learning Framework Across Time Series to Optimize the Energy Consumption of Air Conditioning Systems

In building management, usually static thermal setpoints are used to maintain the inside temperature of a building at a comfortable level irrespective of its occupancy. This strategy can cause a massive amount of energy wastage and therewith increase energy related expenses. This paper explores how to optimise the setpoints used in a particular room during its unoccupied periods using machine learning approaches. We introduce a deep-learning model based on Recurrent Neural Networks (RNN) that can predict the temperatures of a future period directly where a particular room is unoccupied and by using these predicted temperatures, we define the optimal thermal setpoints to be used inside the room during the unoccupied period. We show that RNNs are particularly suitable for this learning task as they enable us to learn across many relatively short series, which is necessary to focus on particular operation modes of the air conditioning (AC) system. We evaluate the prediction accuracy of our RNN model against a set of state-of-the-art models and are able to outperform those by a large margin. We furthermore analyse the usage of our RNN model in optimising the energy consumption of an AC system in a real-world scenario using the temperature data from a university lecture theatre. Based on the simulations, we show that our RNN model can lead to savings around 20% compared with the traditional temperature controlling model that does not use optimisation techniques.

Optimization of building envelopes using indigenous materials to achieve thermal comfort and affordable housing in Abuja, Nigeria

PurposeThis paper aims to demonstrate the optimization of an existing residential building in a tropical climate using indigenous materials as an alternative to conventional building envelopes to achieve thermal comfort and affordable housing.Design/methodology/approachThis study mainly adopted a quantitative research methodology through a comprehensive simulation study on a selected prototype building. The energy plus simulation tool in DesignBuilder was used to predict the average monthly and annual thermal comfort of a typical residential building in the study area. Also, a cost analysis of the final optimization interventions was conducted to estimate the construction cost savings.FindingsThe comparative analysis of simulation results for the base-case and optimized models indicates potential advantages in replacing conventional building envelope materials with indigenous materials. The base-case simulation results showed that the annual operative temperature is more than the adaptive thermal comfort set points in tropical climates, by 8.26%. This often leads to interventions using mechanical cooling systems, thus triggering overconsumption of energy and increase in CO2 emissions. The building envelope materials for floor, walls and roof were replaced with low U-values indigenous materials until considerable results in terms of thermal comfort and overall building construction cost were achieved. The final simulation results showed that using indigenous materials for the ground floor, external walls and roof could substantially reduce the annual operative temperature by 8%, thereby increasing the predicted three months of thermal comfort in the base-case to nine months annually. Likewise, there was a 32.31%, 35.78% and 41.81% reduction in the annual CO2 emissions, cooling loads and construction costs, respectively.Originality/valueThe knowledge of indigenous materials as an alternative to conventional materials for sustainable buildings is not new. However, most of the available research is focused on achieving affordable housing. There is a dearth of research showing the extent that these indigenous materials can be used to improve indoor thermal comfort in developing countries with tropical climates such as Nigeria.

Performance metrics for room air-conditioners: energy, comfort and environmental impacts

India’s current standards and labels for room air-conditioners (RACs) account for energy efficiency, but omit other important criteria that could influence product development and consumer choice: thermal comfort and environmental impact. This study proposes comprehensive RAC evaluation criteria by accounting for the operational energy, thermal comfort and environmental impact through the metrics of the Indian seasonal energy efficiency ratio (ISEER), cooling seasonal energy consumption (CSEC) and total equivalent warming impact (TEWI), respectively. It uses five RAC product cases based on the Indian RAC market to demonstrate the enhanced evaluation criteria across the climatic conditions of 55 Indian cities subject to static and adaptive (air-conditioned, AC; and mixed-mode, MM) thermal setpoints. The analysis yields a significant difference in the number of RAC operational hours between the static and adaptive (AC and MM) methods, resulting in a difference in the overall performance of RACs across varied climatic contexts. <em><strong>Practice relevance</strong></em> RACs are increasingly being adopted as a standard method of active cooling with the modernisation of small and medium-sized commercial and residential spaces in India. However, current standards and labels only provide a partial evaluation of RAC performance. This study targets three aspects of RAC performance in energy, comfort and environmental impacts. It demonstrates that existing RAC performance standards in India can be improved to account for climatic variability and environmental impact in addition to the existing understanding of energy efficiency. The outcomes of this study can serve as inputs to standards and labelling programmes by improving the component-level energy efficiency of RACs, educating the end-users on optimising their thermal setpoints for improved performance, and supporting the development of RACs with low/zero global warming potential refrigerants.

Thermal zoning and window optimization framework for high-rise buildings

Window sizing and configuration can have a significant influence on building energy consumption. Window selection often has a conflicting objective on heating, cooling, and lighting performance. The smaller window performs better on controlling heat loss in winter and solar heat gain in summer, while the larger window performs better on providing views, daylight, and solar heat gains in winter. Also, the energy consumption analyses of high-rise buildings have some fundamental limitations that include the changes in microclimate parameters with altitude, the treatment of building size, uncertainties associated with the existing convective heat transfer coefficients correlations (CHTC). This study provides a framework for simulation-based optimization of window configuration for a high-rise building to minimize its energy consumption. The technique involves CFD modeling to validate and develop new-CHTCs, a Building Energy Simulation used to assess the energy consumption using the newly developed CHTC, and a numerical optimizer for iterative optimal window configuration selection. The decision parameters are window size and room location. The thermal comfort temperature set points and daylight illuminance are taken as constraints. The proposed approach is implemented as a case study on a single by single room model basis positioned at different heights in an isolated 100 m tall building exposed to Boston, MA microclimate. For a room located on the 2nd, 15th, and 29th floor, an optimum window configuration of 30%, 48%, and 30%, window-to-wall ratio, respectively, are obtained.

On the interaction between personal comfort systems and centralized HVAC systems in office buildings

ABSTRACTMost modern HVAC systems in office buildings are unable to meet diverse comfort requirements of the occupants and are not energy efficient. We propose to mitigate both issues by using personal comfort systems (PCS). Specifically, we address the question, ‘How should an existing HVAC system modify its operation to benefit from the deployment of PCSs?’ For example, energy use could be reduced during periods of sparse occupancy by choosing appropriate thermal set points, with each PCS providing the additional offset in thermal comfort required by each occupant. We present the design of a PCS-aware HVAC control strategy based on Model Predictive Control (MPC) that employs a bi-linear thermal model. We use extensive simulations to compare the energy use and comfort offered by our PCS-aware HVAC system with that of a state-of-the-art MPC-based central HVAC system. We study different room layouts and scenarios with full or partial deployment of PCSs. Numerical evaluations show that our system yields significant savings in energy use in both summer and winter, compared both with a state-of-the-art system that does not deploy PCSs and with a similar system that deploys PCSs, but is not aware of them.

Structural collapse in kaolinite, montmorillonite and illite clay and its role in the ceramic rehydroxylation dating of low-fired earthenware

The rehydroxylation dating of ancient pottery estimates the age of ceramic manufacture based upon the total hydroxyl (OH) accumulation since initial firing. The diffusion of OH is impacted by the structural porosity of the ceramic that becomes progressively, or suddenly, closed with increasing temperature as the clay structure collapses. Changes in ceramic mineral structure along the temperature continuum occur at certain thermal set points. Infrared spectroscopic analysis of heat-treated kaolin, illite, and montmorillonite reveals that shifts in the Si-O band correlate with the extent of structural collapse occurring between 600 and 1000 °C. Accelerated rehydroxylation experiments reveal that the activation energy of rehydroxylation decreases with greater structural collapse and indicates that the rate of rehydroxylation will be faster for ceramics fired at more elevated temperatures.

Occupant satisfaction in LEED-certified higher education buildings

Purpose– The purpose of this paper is to quantify occupant satisfaction levels within Leadership in Energy and Environmental Design (LEED)-certified higher education buildings and determine the extent to which this certification helps designers to deliver successful interior environments.Design/methodology/approach– A web-based adaptation of the Center for the Built Environment's (CBE) Occupant indoor environmental quality Survey was distributed within two LEED-certified higher education buildings at a public university. Occupants were then asked to participate in interviews designed to contextualize survey results and further understand issues identified by building users. Descriptive statistics and a content analysis were utilized to determine satisfaction levels in each building.Findings– In general, LEED certification may be used as an aid for delivering successful interior environments, however, opportunities for enhancing the reliability of this tool were identified. For example, while Building A and Building B exceeded the CBE standards, neither met the 80 percent thermal comfort satisfaction rating recommended in the LEED system. Interviewees identified thermal set points, lack of thermal controls, and cold material finishes as the culprits. Based on study findings and supporting literature, suggestions are made for improving the LEED certification process and integrating post-occupancy evaluations (POEs) into the development of higher education buildings.Originality/value– This study's findings may shed some light on how LEED certification and POEs aid in the production of exemplary higher education facilities. This study helps to inform sustainable practices in higher education settings and serve as a foundation for continued research in the field of sustainable design.

Integrating probabilistic methods for describing occupant presence with building energy simulation models

This paper presents a method for developing a probabilistic-based occupancy model that focuses on occupants’ long vacancy activities (greater than 1 week) and other potential building underutilization that is further integrated with a building energy simulation model. The combined model is then applied toward an existing Leadership in Energy and Environmental Design (LEED) certified military dormitory and later compared with corresponding values from the energy model's original prediction as well as actual building energy data. The occupancy model simulates annual building occupancy rates comprised of weekly values based on the frequency, duration, and seasonality of occupants’ long vacancy activities. The energy model uses the simulated occupancy rates to yield the building's predicted range of energy performance. Applying the combined model to the existing LEED building resulted in an improved, predicted Energy Use Intensity (EUI) mean value of 612MJ/m2 as compared to the original model and actual EUI values of 691 and 590, respectively. While the model also demonstrated its utility in describing the change in predicted performance over a range of probabilities associated with certain long vacancy activities, efforts to incorporate other occupant behavior-related aspects such occupant schedules and thermal set points could further improve modeling efforts.

Planck–Benzinger thermal work function: Monoclonal antibody–DNA duplex binding interactions

AbstractWe have reexamined the van't Hoff plots and delineation of thermodynamic data of the monoclonal antibodies of Jel 274 and Jel 241 binding to DNA duplex at high ionic strength using fluorescein‐labeled oligonucleotide titration with increasing concentrations of the antibody as reported by Tanha and Lee (Nucleic Acid Res, 1997, 25, 1442). To compare the thermodynamic parameters from data over the experimental temperature range of 277–312.5 K, the binding constant from van't Hoff plots is used to evaluate ΔGo(T) from 0 to 400 K using our general linear T3 model, ΔGo(T) = α +βT2+γT3. The limited information provided by the van't Hoff plots and their extensions is not sufficient to describe the variations in the Gibbs free energy change as a function of temperature and other thermodynamic functions observed in these and other biological interactions. Rather, it is necessary to determine a number of thermodynamic parameters, including the heat of reaction, (Th), (Tm), and (TCp), and the thermal set point, (TS), all of which can be precisely assessed using our general linear T3 model. To date, no experimental measurement offers this degree of accuracy. In evaluating the thermodynamic parameters in the binding interaction of monoclonal IgG Jel 241–d[AT]20DNA duplex, it is apparent that at a high NaCl concentration, the range of the compensatory temperatures, (Th) = 155 K and (Tm) = 450 K, is much broader than observed in any other sample, whereas the thermal set points, (TS) = 330 K, is 20–30 K higher. The inherent chemical bond energy ΔHo(T0) is much lower in this sample. The values of thermal agitation energy (heat capacity integrals) are of similar magnitude for all the samples tested. It appears that increasing the NaCl concentration to 130 mM will greatly enhance the binding interaction between the monoclonal antibody and DNA duplex. It is not clear, however, from the limited data available, whether the binding interaction is sequence specific, although logic would suggest it is. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009

The Effect of Thermal Quality on the Thermoregulatory Behavior of the Bearded DragonPogona vitticeps: Influences of Methodological Assessment

Metabolic functions are generally optimized within a narrow range of body temperatures (T(b)'s), conferring thermoregulation great importance to the survival and fitness of an animal. In lizards, T(b) regulation is mainly behavioral, and the metabolic costs associated with behavioral thermoregulation are primarily locomotory. In reptiles, however, it has been proposed that they thermoregulate less precisely when the associated costs, metabolic or otherwise, are high. Such a strategy enhances fitness by allowing lizards to be more flexible to changing environmental conditions while maximizing the benefits of maintaining a high T(b) and minimizing energy expenditure. We evaluated the behavioral thermoregulation of inland bearded dragons Pogona vitticeps under various thermal quality conditions requiring different locomotory investment for thermoregulation. The selected ambient temperature and preferred T(b) ranges increased at lower environmental thermal qualities, indicating a decrease in thermoregulatory precision in environments where the costs associated with thermoregulation were high. The level of thermoregulation was also affected, exhibiting a decrease in preferred T(b) of approximately 2 degrees C at the lowest-thermal-quality treatment. These data provide important implications for the procedural assessment of preferred T(b) and a better understanding of thermal set points in reptiles in general. Our results emphasize that the precise maintenance and assessment of preferred T(b) is contingent on the quality of the environment, laboratory or natural, that the animal inhabits.

Effects of Post-Hatching Maintenance Temperature on Desert Tortoise (Gopherus agassizii) Shell Morphology and Thermoregulatory Behavior

Effects of Post-Hatching Maintenance Temperature on Desert Tortoise (Gopherus agassizii) Shell Morphology and Thermoregulatory Behavior