Showing papers on "Thermostat published in 2019"
TL;DR: The combination of artificial neural network and multi-objective genetic algorithm is applied to optimize the two-chiller system operation in a building to show the best result regarding thermal comfort and energy consumption compared to base case design.
138 citations
TL;DR: In this paper, the Langevin thermostat outperforms the Nose-Hoover (chain) thermometer in NEMD simulations because of its stochastic and local nature, which is particularly important for studying asymmetric carbon-based nanostructures.
Abstract: Nonequilibrium molecular dynamics (NEMD) has been extensively used to study thermal transport at various length scales in many materials. In this method, two local thermostats at different temperatures are used to generate a nonequilibrium steady state with a constant heat flux. Conventionally, the thermal conductivity of a finite system is calculated as the ratio between the heat flux and the temperature gradient extracted from the linear part of the temperature profile away from the local thermostats. Here we show that, with a proper choice of the thermostat, the nonlinear part of the temperature profile should actually not be excluded in thermal transport calculations. We compare NEMD results against those from the atomistic Green's function method in the ballistic regime, and those from the homogeneous nonequilibrium molecular dynamics method in the ballistic-to-diffusive regime. These comparisons suggest that in all the transport regimes, one should directly calculate the thermal conductance from the temperature difference between the heat source and sink and, if needed, convert it to the thermal conductivity by multiplying it with the system length. Furthermore, we find that the Langevin thermostat outperforms the Nose-Hoover (chain) thermostat in NEMD simulations because of its stochastic and local nature. We show that this is particularly important for studying asymmetric carbon-based nanostructures, for which the Nose-Hoover thermostat can produce artifacts leading to unphysical thermal rectification. Our findings are important to obtain correct results from molecular dynamics simulations of nanoscale heat transport as the accuracy of the interatomic potentials is rapidly improving.
103 citations
TL;DR: In this paper, the Langevin thermostat outperforms the Nose-Hoover (chain) in NEMD simulations because of its stochastic and local nature, which is particularly important for studying asymmetric carbon-based nanostructures.
Abstract: Nonequilibrium molecular dynamics (NEMD) has been extensively used to study thermal transport at various length scales in many materials. In this method, two local thermostats at different temperatures are used to generate a nonequilibrium steady state with a constant heat flux. Conventionally, the thermal conductivity of a finite system is calculated as the ratio between the heat flux and the temperature gradient extracted from the linear part of the temperature profile away from the local thermostats. Here, we show that, with a proper choice of the thermostat, the nonlinear part of the temperature profile should actually not be excluded in thermal transport calculations. We compare NEMD results against those from the atomistic Green’s function method in the ballistic regime and those from the homogeneous nonequilibrium molecular dynamics method in the ballistic-to-diffusive regime. These comparisons suggest that in all the transport regimes, one should directly calculate the thermal conductance from the temperature difference between the heat source and sink and, if needed, convert it into the thermal conductivity by multiplying it with the system length. Furthermore, we find that the Langevin thermostat outperforms the Nose-Hoover (chain) thermostat in NEMD simulations because of its stochastic and local nature. We show that this is particularly important for studying asymmetric carbon-based nanostructures, for which the Nose-Hoover thermostat can produce artifacts leading to unphysical thermal rectification.
99 citations
TL;DR: In this paper, the authors investigated the impact of personal thermal comfort sensitivities on collective conditioning and found that thermal comfort sensitivity plays a statistically significant role in collective conditioning as it resulted in changes of temperature setpoint in 86% of cases and a higher probability of achieving collective comfort.
73 citations
TL;DR: The adaptive modelling and robust optimization of the ARMPC prevent the indoor condition from violating the constrains due to model inaccuracy and uncertainties in measured disturbances.
39 citations
TL;DR: In this paper, the authors used the adaptive thermal comfort model to estimate the number of hours per year required for cooling and heating to sustain the occupants' thermal comfort for four full-scale housing test modules at the campus of the University of Newcastle, Australia.
Abstract: The building industry is regarded a major contributor to climate change as energy consumption from buildings accounts for 40% of the total energy. The types of thermal comfort models used to predict the heating and cooling loads are critical to save energy in operative buildings and reduce greenhouse gas emissions (GHG). In this research, the internal air temperatures were recorded for over one year under the free floating mode with no heating or cooling, then the number of hours required for heating or cooling were calculated based on fixed sets of operative temperatures (18 °C–24 °C) and the adaptive thermal comfort model to estimate the number of hours per year required for cooling and heating to sustain the occupants’ thermal comfort for four full-scale housing test modules at the campus of the University of Newcastle, Australia. The adaptive thermal comfort model significantly reduced the time necessary for mechanical cooling and heating by more than half when compared with the constant thermostat setting used by the air-conditioning systems installed on the site. It was found that the air-conditioning system with operational temperature setups using the adaptive thermal comfort model at 80% acceptability limits required almost half the operating energy when compared with fixed sets of operating temperatures. This can be achieved by applying a broader range of acceptable temperature limits and using techniques that require minimal energy to sustain the occupants’ thermal comfort.
34 citations
TL;DR: In this article, the authors present an inverse modeling approach which inverses the zone air heat balance equation and uses the measured zone air temperature to analytically calculate the Zone air infiltration rate and Zone internal thermal mass (e.g., furniture, interior partitions).
34 citations
TL;DR: In this article, a passive radiative "thermostat" based on phase-change photonic nanostructures for thermal regulation at room temperature is proposed, which uses the sky to passively cool or heat during day-time using the phase change transition temperature as the setpoint, while at night-time temperature is maintained at or below ambient.
Abstract: A thermostat senses the temperature of a physical system and switches heating or cooling devices on or off, regulating the flow of heat to maintain the system's temperature near a desired setpoint. Taking advantage of recent advances in radiative heat transfer technologies, here we propose a passive radiative "thermostat" based on phase-change photonic nanostructures for thermal regulation at room temperature. By self-adjusting their visible to mid-IR absorptivity and emissivity responses depending on the ambient temperature, the proposed devices use the sky to passively cool or heat during day-time using the phase-change transition temperature as the setpoint, while at night-time temperature is maintained at or below ambient. We simulate the performance of a passive nanophotonic thermostat design based on vanadium dioxide thin films, showing daytime passive cooling (heating) with respect to ambient in hot (cold) days, maintaining an equilibrium temperature approximately locked within the phase transition region. Passive radiative thermostats can potentially enable novel thermal management technologies, e.g. to moderate diurnal temperature in regions with extreme annual thermal swings.
31 citations
TL;DR: The EnergyPlus program was used to simulate the energy consumption of HVAC systems in office buildings and a behavioral artificial neural network model was implemented in the energy simulation, which calculated a wider comfort zone and a higher variation in energy use due to occupant behavior.
31 citations
TL;DR: A co-simulation framework to rapidly model and simulate building energy use and optimize cooling setpoint controls uses a model predictive control formulation capable of reducing cooling electricity costs by up to 30%; however, cost savings and peak demand shedding are highly dependent on the time-of-use electricity rate schedule.
31 citations
TL;DR: In this article, simulations have been performed based on three heating and cooling systems in three different geographical locations (Copenhagen, Denmark, Paris, France and Rome, Italy).
TL;DR: A new temperature-grouped dual Nose-Hoover thermostat is presented, where the molecular center of mass translations are assigned to a temperature group separated from the rest degrees of freedom, and it is demonstrated that this scheme predicts correct static and dynamic properties for all the systems tested here, regardless of thethermostat coupling strength.
Abstract: An explicit treatment of electronic polarization is critically important to accurate simulations of highly charged or interfacial systems. Compared to the iterative self-consistent field (SCF) scheme, extended Lagrangian approaches are computationally more efficient for simulations that employ a polarizable force field. However, an appropriate thermostat must be chosen to minimize heat flow and ensure an equipartition of kinetic energy among all unconstrained system degrees of freedom. Here we investigate the effects of different thermostats on the simulation of condensed phase systems with the Drude polarizable force field using several examples that include water, NaCl/water, acetone, and an ionic liquid (IL) BMIM+/BF4-. We show that conventional dual-temperature thermostat schemes often suffer from violations of equipartitioning and adiabatic electronic state, leading to considerable errors in both static and dynamic properties. Heat flow from the real degrees of freedom to the Drude degrees of freedom leads to a steady temperature gradient and puts the system at an incorrect effective temperature. Systems with high-frequency internal degrees of freedom such as planar improper dihedrals or C-H bond stretches are most vulnerable; this issue has been largely overlooked in the literature because of the primary focus on simulations of rigid water molecules. We present a new temperature-grouped dual-Nose-Hoover thermostat, where the molecular center of mass translations are assigned to a temperature group separated from the rest degrees of freedom. We demonstrate that this scheme predicts correct static and dynamic properties for all the systems tested here, regardless of the thermostat coupling strength. This new thermostat has been implemented into the GPU-accelerated OpenMM simulation package and maintains a significant speedup relative to the SCF scheme.
TL;DR: In this paper, a new temperature and hydraulic-balance control strategy is proposed for air-source heat pump (ASHP) heating systems, which utilizes the cooperation of three different devices: room thermostats, pressure independent balancing and control valves, and a differential pressure relief controller.
TL;DR: In this article, a temperature-trapping theory for coupled thermoelectric fields was developed and a negative energy thermostat was proposed, which can convert ambient thermal energy into electricity without loss of its thermostatic ability.
Abstract: A substantial fraction of our energy consumption is due simply to maintaining a desired constant temperature. Based on an existing approach for energy-free temperature maintenance, the authors develop a temperature-trapping theory for coupled thermoelectric fields and propose a ``negative energy thermostat'', which can convert ambient thermal energy into electricity without loss of its thermostatic ability. They also design a thermoelectric thermostatic cloak, which could be interesting for designing energy-saving buildings, vehicles, and spacecraft.
TL;DR: An optimization methodology using coupled simulation of the airflow and HVAC that captures the dynamics of both systems is proposed, and it is found that the optimal location of thermostat can be determined to achieve either best thermal comfort or least energy consumption.
TL;DR: GJF is a thermodynamically sound variation on the Stormer-Verlet algorithm for simulating discrete-time Lang... as discussed by the authors, which is used in the GJF thermostat.
Abstract: We expand on the previously published Gronbech-Jensen Farago (GJF) thermostat, which is a thermodynamically sound variation on the Stormer-Verlet algorithm for simulating discrete-time Lang...
TL;DR: This work studies the existence, multiplicity, and uniqueness results of positive solutions for a fractional thermostat model that depends on the fixed point index theory, iterative method, and nonsymmetry property of the Green function.
Abstract: We study the existence, multiplicity, and uniqueness results of positive solutions for a fractional thermostat model. Our approach depends on the fixed point index theory, iterative method, and nonsymmetry property of the Green function. The properties of positive solutions depending on a parameter are also discussed.
01 Oct 2019
TL;DR: A MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings is proposed and Fanger’s Predicted Mean Vote (PMV) as thermal comfort index is used, while to predict the energy performance of the building, it is adopted a simplified thermal model.
Abstract: Model Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger’s Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats.
29 Mar 2019
TL;DR: Greina is proposed - to continuously monitor the readily available ambient information from the thermostat and timely report such leaks of refrigerant leakage in smart thermostats.
Abstract: Smart thermostats, with multiple sensory abilities, are becoming pervasive and ubiquitous, in both residential and commercial buildings. By analyzing occupants' behavior, adjusting set temperature automatically, and adapting to temporal and spatial changes in the atmosphere, smart thermostats can maximize both - energy savings and user comfort. In this paper, we study smart thermostats for refrigerant leakage detection. Retail outlets, such as milk-booths and quick service restaurants set up cold-rooms to store perishable items. In each room, a refrigeration unit (akin to air-conditioners) is used to maintain a suitable temperature for the stored products. Often, refrigerant leaks through the coils (or valves) of the refrigeration unit which slowly diminishes the cooling capacity of the refrigeration unit while allowing it to be functional. Such leaks waste significant energy, risk occupants' health, and impact the quality of stored perishable products. While store managers usually fail to sense the early symptoms of such leaks, current techniques to report refrigerant leakage are often not scalable. We propose Greina - to continuously monitor the readily available ambient information from the thermostat and timely report such leaks. We evaluate our approach on 74 outlets of a retail enterprise and results indicate that Greina can report the leakage a week in advance when compared to manual reporting.
TL;DR: In this article, a near-surface embedded electrical heating grid prototype is investigated through laboratory and field testing for concrete pavement heating, providing an airfield pavement anti-icing alternative, and the prototype grid, installed on small-scale concrete test mats, was studied in a laboratory controlled below-freezing temperature environment.
Abstract: Snow and ice on airfield pavement threatens aircraft ground operation safety. Plowing and chemical treatment are used for snow removal, but yield long-term detrimental impacts to the airfield infrastructure and environment. In this paper, a “near-surface embedded electrical heating grid prototype” is investigated through laboratory and field testing for concrete pavement heating, providing an airfield pavement anti-icing alternative. The prototype grid, installed on small-scale concrete test mats, was studied in a laboratory controlled below-freezing temperature environment. Testing evaluated pavement surface heating performance under two energy supply methods: (1) an alternating heating sequence and (2) an automated thermostat heating sequence, assessing their ability to (1) raise the pavement surface temperature to an anti-icing temperature threshold, 2 °C, and (2) sustain an anti-icing surface temperature. The alternating heating sequence required a low power input, but an increased heating time. Under the automated thermostat heating sequence, with a 152.4 mm parallel heat wire spacing and 667 W/m2 power input, surface temperature rose from an initial −12 °C to 2 °C in 4 h, then maintained in the anti-icing range, 2 °C to 5 °C. Laboratory, preliminary study results directed the construction, instrumentation, and operation of a large-scale prototype slab for field testing. The prototype was built in Fayetteville, AR and subjected to ambient outdoor climate conditions. The full-scale testing used a photovoltaic energy system as the power source. Field testing assessed heating/anti-icing performance and energy consumption. During tests in below freezing air temperatures and snow events, the photovoltaic energy system supplied enough energy to maintain the large-scale prototype slab surface above 0 °C.
TL;DR: The integrated consideration of engine friction and actuator power is presented to minimize engine fuel consumption and a real-time implementation of the proposed strategy on a hardware platform using model predictive control (MPC) with a limited horizon is presented, which shows the feasibility of the proposal.
Abstract: The engine friction and actuator power are the main factors of the cooling system affecting the fuel economy of a spark ignition (SI) engine. An electrified cooling system containing an electric fan, pump, and thermostat provides an opportunity to reduce fuel consumption. The coolant temperature is always kept at a high fixed value within the safe temperature range to avoid friction losses caused by overcooling; however, the actuator power is not typically considered. Recent publications have attempted to minimize the actuator power and the coolant temperature is maintained in a range. Nevertheless, neither method quantitatively considers both factors. In this paper, the integrated consideration of engine friction and actuator power is presented to minimize engine fuel consumption. The accuracy of a control-oriented model of a cooling system is improved first in an attempt to exert the full potential of the model. Then, the proposed strategy for minimum fuel consumption is constructed as an optimization problem and the improvement of fuel economy obtained by the proposed strategy is evaluated using a causal suboptimal controller and dynamic programming (DP)-based global optimal controller. Compared with a causal coolant temperature tracking controller, the causal suboptimal controller and the global optimal controller based on the proposed strategy both achieve significant improvements. Compared with a global optimal controller for minimum actuator power, the global optimal controller based on the proposed strategy achieves a certain improvement and this effect can increase as the environmental temperature decreases. Finally, a real-time implementation of the proposed strategy on a hardware platform using model predictive control (MPC) with a limited horizon is presented, which shows the feasibility of the proposed strategy.
13 Nov 2019
TL;DR: Three methods for assessing heating characteristics of households using a dataset that does not contain heating power are presented and a positive linear correlation between characteristics derived for each method indicates that the methods can be used to ascertain relative values for the thermal characteristics of a building.
Abstract: The development of quantitative techniques for determining the amount of heat lost through the building envelope is essential for targeted retrofits. This type of evaluation is traditionally a resource intensive process that involves onsite appraisal and in-situ measurements. In order to build more efficient and scalable methods for retrofit analysis, new sources of data could be used. Smart thermostat data, for example, provide a valuable resource, however they often lack detailed information about the building characteristics and energy loads. This paper presents and compares three methods for assessing heating characteristics of households using a dataset that does not contain heating power. The three methods are based on: (1) balance point plots, (2) the extraction of indoor temperature decay curves, and (3) the classic differential equation for indoor temperature. These methods all take a gray box approach in which physics-based and machine learning models are combined. The dataset used for this study consists of over 4,000 houses in Ontario and New York. The three methods are applied to each building and the resulting data is analyzed to determine whether the results are statistically sound. It is found that there is a positive linear correlation between characteristics derived for each method, although there is uncertainty about absolute values. This result indicates that the methods can be used to ascertain relative values for the thermal characteristics of a building. The methods suggested in this paper may therefore be used to filter heating profiles to target potential retrofit measures or other stock-level decisions.
TL;DR: Study of the variation in the power dissipated by the human body surface as a function of time for a thermostat temperature of 28 °C shows that the heat flux in the trunk is much more stable than that in the hand and that theHeat flux inThe sternum is greater than thatin other areas.
TL;DR: This research investigates the deployment of room-specific demand response in a district-heated office building in Southern Finland by controlling set-point temperatures on thermostatic radiator valves and presents findings of load shifting on room level and about local discomfort.
TL;DR: Two types of interactive feedback integrated with thermostat interfaces are proposed and compared: feedback of energy efficiency, and feedback of health level of the current temperature setting, showing that both resulted in higher temperature setting and preference than interfaces without feedback.
01 Sep 2019
05 May 2019
TL;DR: This paper optimizes the battery schedule to minimize the monthly electricity bill and proposes a novel mathematical model for calculating HVAC power consumption with a given thermostat schedule.
Abstract: The objective of this project is to develop a load forecasting technique and demand management algorithm for a building to schedule battery and Heating Ventilation Air Conditioning system (HVAC) using the Model Predictive Control (MPC). Behind-the-meter energy storage is used for modifying the load shape and minimizing the demand charge of a building. Thermal mass of the building can also be utilized to store the heat/cool energy and HVAC is scheduled to minimize power consumption during peak times. This paper optimizes the battery schedule to minimize the monthly electricity bill. The load profile has to be forecasted and this algorithm uses a two-part forecaster where a deterministic part uses exponentially weighted moving average (EWMA) model accounting for longer term trends and a second order regression model (AR2) accounting for the short term variations. A novel mathematical model has been proposed for calculating HVAC power consumption with a given thermostat schedule. Greater savings can be realized by augmenting this algorithm with HVAC scheduling and authors are working on it minimize HVAC power consumption during peak hours without causing thermal discomfort to the residents of the building.
13 Nov 2019
TL;DR: It is validated that a small number of pre-trained neural network models is enough to develop a sufficiently accurate RC model for any home across the country and that this model outperforms a seasonal time-series model that is built using the same amount of data.
Abstract: Modelling temperature dynamics of a building is necessary to develop control mechanisms for reducing energy consumption of heating and cooling equipment. While Resistance-Capacitance (RC) models can accurately explain how the indoor temperature changes over time, building such models requires the knowledge of the building insulation and thermal mass, which is not readily available for most residential buildings in operation today. In the absence of this information, model parameters can be estimated from coarsegrained data collected by smart thermostats. In this paper we train a Bayesian neural network to establish the RC model for a home equipped with a smart thermostat, and investigate how to reuse this model to predict the temperature inside another home which may not be equipped with a smart thermostat. Leveraging data from ecobee smart thermostats installed in over 4,000 homes in Canada, we validate that a small number of pre-trained neural network models is enough to develop a sufficiently accurate RC model for any home across the country and that this model outperforms a seasonal time-series model that is built using the same amount of data.
TL;DR: The model is used to explore the diffusive motion of a thermalized charge in a weak magnetic field and the associated Hall and Pedersen mobilities, where over a range of magnetic field strengths the charge exhibits absolute negative mobility.
Abstract: A two-dimensional version of a chaotic thermostat is investigated. Its structure follows the concept previously introduced by the author [G. J. Morales, Phys. Rev. E 97, 032203 (2018)2470-004510.1103/PhysRevE.97.032203] to generate a one-dimensional chaotic thermostat, namely, the usual friction force of a deterministic thermostat is supplemented with a self-consistent fluctuating force that depends on the drag coefficient associated with coupling to the heat bath. Azimuthal symmetry requires the thermostat to have two internal degrees of freedom, thus the Martyna-Klein-Tuckerman [G. J. Martyna et al., J. Chem. Phys. 97, 2635 (1992)JCPSA60021-960610.1063/1.463940] model is chosen for the heat bath. The unmagnetized system exhibits two-dimensional diffusive behavior, achieves symmetric Maxwellian velocity distributions in the absence of an external potential, and satisfies the Einstein relation when an external force is applied. The velocity fluctuations display the characteristic exponential frequency spectrum associated with chaotic systems. The model is used to explore the diffusive motion of a thermalized charge in a weak magnetic field and the associated Hall and Pedersen mobilities. Over a range of magnetic field strengths the charge exhibits absolute negative mobility.
01 Oct 2019
TL;DR: This paper presents a simplified data-driven approach for providing frequency regulation using aggregated residential HVAC units and shows that while the results are satisfactory using PJM's performance metrics, improvements can still be made by accounting for model errors.
Abstract: The growing level of integration of renewable energy sources into power grids around the world has increased the need for cheap, reliable and effective fast frequency regulation resources. On that note, most grid operators are already considering the usage of demand-side resources for frequency regulation purposes. A major candidate for such initiatives is the heating, ventilation and air conditioning (HVAC) unit in both residential and commercial buildings. In this paper, we present a simplified data-driven approach for providing frequency regulation using aggregated residential HVAC units. The approach involves the use of a residential load model to generate data for frequency regulation model identification. Afterward, a relationship between instantaneous aggregated HVAC power consumption, HVAC power changes and thermostat setpoint offsets is established using a simple multiple linear regression model. Actual regulation qualification signals from the PJM market and the regression model are then used to evaluate the ability of the units to satisfactorily respond to frequency regulation signals. The obtained results show that while the results are satisfactory using PJM's performance metrics, improvements can still be made by accounting for model errors.