Showing papers on "Thermostat published in 2022"
TL;DR: In this paper , a neural network is used to construct a strategic framework to obtain discrete control actions, that is, thermostat settings, and a new objective function truncation method is adopted to limit the update step size and enhance the robustness of the algorithm.
24 citations
TL;DR: In this paper , the authors compared the effect of different types of thermostats (e.g. Andersen, stochastic dynamics (SD), Berendsen, V-rescale, Nosé-Hoover, and Parrinello-Rahman barostats) on a wide range of simulated properties.
24 citations
TL;DR: In this article , a quantitative assessment of the impact of different users in residential heating consumption and indoor environmental conditions has been provided, where heating energy consumption profiles for real users along with their corresponding thermostat settings were modeled in a building simulation model of a single dwelling thus allowing the study of the user factor in isolation.
20 citations
TL;DR: In this article , the authors examined whether linear-form prediction models are reliable to support the model predictive control (MPC) of residential buildings equipped with single types of heating systems, and developed linear form models, namely an autoregressive with exogenous inputs (ARX) for predicting the indoor temperature and threshold-piecewise models for the return and supply water temperatures.
16 citations
TL;DR: In this paper , a DQN based multi-objective optimal control strategy is designed for temperature setpoint real-time reset to balance the energy consumption and indoor air temperature.
15 citations
TL;DR: In this paper , the authors proposed an ML-based MPC with an instantaneous linearization (IL) scheme, which employs real-time building operation data to linearize the nonlinear MPC for constructing a linear MPC at each control interval.
14 citations
TL;DR: In this paper, the authors proposed an ML-based MPC with an instantaneous linearization (IL) scheme, which employs real-time building operation data to linearize the nonlinear MLbased building model for constructing a linear MPC at each control interval.
14 citations
TL;DR: In this article , an agent-based model is developed to simulate the in-home and out-of-home activities of individuals in order to understand the implications of imposed COVID-19 lockdown with respect to energy usage in residential locations.
14 citations
TL;DR: In this paper , a new method was developed to optimize the setpoint temperature of thermostats in different climates of Iran, where numerical modeling by EnergyPlus software, Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN), and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was used to train the neural network.
14 citations
TL;DR: In this article , a performance comparison of the same thermoelectric generator prototype applied in a conventional vehicle and an extended-range electric vehicle is conducted through both simulation and experimental methods.
13 citations
TL;DR: In this article , the authors analyzed the energy-saving potential and economic viability of adopting active energy efficiency measures (EEMs) on a detached family house in warm-humid climate of South India.
TL;DR: In this paper , the authors explored sensor fault impacts on different HVACs, the thermostat offset fault was taken as an example, and compared its influences on operational performance of three HVCs including the ground source heat pump (GSHP), the variable refrigerant flow (VRF) system and the chiller + boiler (CB) system.
TL;DR: In this article , the existence, uniqueness, and stability of positive solution in relation to a fractional version of variable order thermostat model equipped with nonlocal boundary values in the Caputo sense are investigated.
Abstract: In the current manuscript, we intend to investigate the existence, uniqueness, and the stability of positive solution in relation to a fractional version of variable order thermostat model equipped with nonlocal boundary values in the Caputo sense. In fact, we will get help from the constant piece-wise functions for transforming our variable order model into an auxiliary standard model of thermostat. By Guo-Krasnoselskii’s fixed point theorem on cones, we derive the required conditions ensuring the existence property for positive solutions. An example is illustrated to examine the validity of the observed results.
TL;DR: In this paper , the authors report frequently overlooked errors in thermostated or constant-temperature DFT-MD simulations applied to study (electro)catalytic chemistry and conclude that the flying ice cube effect in these systems can be conveniently avoided using Langevin dynamics.
Abstract: Density functional theory-based molecular dynamics (DFT-MD) has been widely used for studying the chemistry of heterogeneous interfacial systems under operational conditions. We report frequently overlooked errors in thermostated or constant-temperature DFT-MD simulations applied to study (electro)catalytic chemistry. Our results demonstrate that commonly used thermostats such as Nosé–Hoover, Berendsen, and simple velocity-rescaling methods fail to provide a reliable temperature description for systems considered. Instead, nonconstant temperatures and large temperature gradients within the different parts of the system are observed. The errors are not a “feature” of any particular code but are present in several ab initio molecular dynamics implementations. This uneven temperature distribution, due to inadequate thermostatting, is well-known in the classical MD community, where it is ascribed to the failure in kinetic energy equipartition among different degrees of freedom in heterogeneous systems (Harvey et al. J. Comput. Chem.1998, 726−740) and termed the flying ice cube effect. We provide tantamount evidence that interfacial systems are susceptible to substantial flying ice cube effects and demonstrate that the traditional Nosé–Hoover and Berendsen thermostats should be applied with care when simulating, for example, catalytic properties or structures of solvated interfaces and supported clusters. We conclude that the flying ice cube effect in these systems can be conveniently avoided using Langevin dynamics.
TL;DR: Six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy.
Abstract: Energy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO2. As a result, some new research areas need to be explored to create innovative energy-saving alternatives in electrical devices that have high energy consumption. One research area of interest is the computer visual classification for reducing energy consumption and keeping thermal comfort in thermostats. Usually, connected thermostats obrtain information from sensors for detecting persons and scheduling autonomous operations to save energy. However, there is a lack of knowledge of how computer vision can be deployed in embedded digital systems to analyze clothing insulation in connected thermostats to reduce energy consumption and keep thermal comfort. The clothing classification algorithm embedded in a digital system for saving energy could be a companion device in connected thermostats to obtain the clothing insulation. Currently, there is no connected thermostat in the market using complementary computer visual classification systems to analyze the clothing insulation factor. Hence, this proposal aims to develop and evaluate an embedded real-time clothing classifier that could help to improve the efficiency of heating and ventilation air conditioning systems in homes or buildings. This paper compares six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models. In addition, the paper describes how the classifier could interact with the thermostat to tune the temperature set point to save energy and keep thermal comfort. The results confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy.
TL;DR: In this paper, the effects of heating powers and heating modes on frozen startup and heat transfer characteristics are experimentally examined and a cesium heat pipe is fabricated and it is performance under the horizontal state is determined.
TL;DR: In this paper , the authors use real world data from the Donate Your Data dataset to analyse the interaction of users with the thermostat around a demand response (DR) event.
TL;DR: In this article , the authors proposed resetting strategies to improve the efficiency of variable-air volume (VAV) systems in buildings, which can provide stable control performance in actual systems as well as achieving the anticipated reheat and fan energy savings.
Abstract: Buildings account for a large portion of the total energy use in the US; therefore, improving the operation of typical variable-air-volume (VAV) systems in buildings can provide a tremendous economic opportunity. ASHRAE Guideline 36 recommends a resetting strategy for supply air temperature (SAT) for VAV systems based on outside air temperature. However, this strategy may not produce optimal performance, particularly when simultaneous cooling and heating occurs in zones. In addition, there is no strategy recommended in the Guideline to reset the zone minimum airflow set point in a single-duct VAV terminal unit with reheat, although this setpoint has a great impact on zone reheat requirements and ventilation efficiency. Thus, this paper introduces new strategies to reset both the SAT and zone minimum airflow rate set points to improve the efficiency of typical VAV systems. The strategies were tested under various conditions through experiments performed in fully instrumented VAV systems located in the HVAC lab at the University of Cincinnati. The experiments were conducted on a chilled-water VAV system that serves three controlled zones with hot-water reheat VAV boxes controlled by a typical commercial BACnet web-based building automation system BAS. The simulation studies were performed using the building energy simulation software EnergyPlus to evaluate the strategies at a larger scale in various locations. The simulation results show that the proposed resetting strategies can provide fan energy savings between 1.6% and 5.7% and heating load savings between 7.7% to 33.7%, depending on the location. The laboratory testing shows that the proposed strategies can provide stable control performance in actual systems as well as achieving the anticipated reheat and fan energy savings. The result offers significant improvements that can be implemented in the Guideline for single-duct VAV system operation and control.
TL;DR: In this paper , the authors study the energy exchanges during erasure processes and model quantitatively the cost of fast operation within the stochastic thermodynamics framework, finding the origins of the overhead to Landauer's bound required for fast operations: in the overdamped regime this term mainly comes from dissipation, while in the underdamped regime it stems from the heating of the memory.
Abstract: Using a double-well potential as a physical memory, we study with experiments and numerical simulations the energy exchanges during erasure processes, and model quantitatively the cost of fast operation. Within the stochastic thermodynamics framework we find the origins of the overhead to Landauer's bound required for fast operations: in the overdamped regime this term mainly comes from the dissipation, while in the underdamped regime it stems from the heating of the memory. Indeed, the system is thermalized with its environment at all times during quasistatic protocols, but for fast ones, the inefficient heat transfer to the thermostat is delayed with respect to the work influx, resulting in a transient temperature rise. The warming, quantitatively described by a comprehensive statistical physics description of the erasure process, is noticeable on both the kinetic and potential energy: they no longer comply with equipartition. The mean work and heat to erase the information therefore increase accordingly. They are both bounded by an effective Landauer's limit k_{B}T_{eff}ln2, where T_{eff} is a weighted average of the actual temperature of the memory during the process.
TL;DR: In this article , a Phase Change Material (PCMixture of animal fat and paraffin wax was thermally characterized, and integrated into a conventional DHW electric heater to enhance its energy performance.
Abstract: In this study, a new lower cost Phase Change Material “PCM” has been developed and valued which will reduce the financial costs related to the domestic hot water system “DHW”. PCM mixture composed of animal fat and paraffin wax was thermally characterized, and integrated into a conventional DHW electric heater to enhance its energy performance. Using the differential scanning calorimetry (DSC) device, the melting range for this new PCM ranged between 35.58 °C and 62.58 °C with a latent heat of fusion between 180 and 210 kJ/kg, making the mixture ideal for DHW use. The new assembled PCM-embedded water tank was experimentally tested in the lab under different operation regimes to evaluate its technical and economic feasibility. Several parameters have been analyzed, including the produced hot water volume, savings in energy consumption, and the associated energy cost. The experimental results indicated that the PCM-enhanced water tank may produce one-third to two times more hot water than the conventional water tank, depending on the thermostat setpoints. In addition, this tank was found to reduce the energy cost by 3.94% and 56.88% at 50 °C and 75 °C, respectively, suggesting that the PCM-enhanced tank should be operated at a higher setpoint to achieve maximum cost benefits.
TL;DR: In this article , a hierarchical model predictive control (MPC) solution is proposed for small and medium-sized commercial buildings (SMCB), where an upper level MPC is responsible for electrical load shifting in response to an electric price signal, while a lower level MPc is responsible to coordinate compressor stages to eliminate unnecessary peaks and follow the setpoints determined by the upper level. But, this approach can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands.
TL;DR: In this paper , the authors evaluated the impacts of HVAC operational faults on system energy and occupant thermal comfort under the current, 2030s and 2050s climates using a validated model, and the energy and thermal comfort impact indicators were proposed to rank single and multiple faults under each climate period.
TL;DR: In this paper, conditions under which a Mpemba-like effect emerges in granular gases of inelastic and rough hard disks driven by a class of thermostats characterized by the splitting of the noise intensity into translational and rotational counterparts are studied.
Abstract: We study the conditions under which a Mpemba-like effect emerges in granular gases of inelastic and rough hard disks driven by a class of thermostats characterized by the splitting of the noise intensity into translational and rotational counterparts. Thus, granular particles are affected by a stochastic force and a stochastic torque, which inject translational and rotational energy, respectively. We realize that a certain choice of a thermostat of this class can be characterized just by the total intensity and the fraction of noise transferred to the rotational degree of freedom with respect to the translational ones. Firstly, Mpemba effect is characterized by the appearance of a crossing between the temperature curves of the considered samples. Later, an overshoot of the temperature evolution with respect to the steady-state value is observed and the mechanism of Mpemba effect generation is changed. The choice of parameters allows us to design plausible protocols based on these thermostats for generating the initial states to observe the Mpemba-like effect in experiments. In order to obtain explicit results, we use a well-founded Maxwellian approximation for the evolution dynamics and the steady-state quantities. Finally, theoretical results are compared with direct simulation Monte Carlo and molecular dynamics results, and a very good agreement is found.
TL;DR: In this article, a novel control method based on the use of Artificial Neural Networks to optimize the operation of refrigeration systems equipped with a fixed-speed compressor is proposed, which can provide the ON/OFF control loop with the optimal hysteresis value accordingly to the requirement of the user, in terms of set-point temperature and optimization priority, and the ambient temperature.
TL;DR: In this paper , the authors predict a model for CO2 emissions and optimize it for 6 cities of the U.S with different climates, using the time series of GMDH type of artificial neural network, the amount of these pollutants was predicted monthly and annually from 2020 to 2025.
TL;DR: In this paper , a second-order fractional difference equation with one sensor and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz type inequality is proposed.
Abstract: Abstract A thermostat model described by a second-order fractional difference equation is proposed in this paper with one sensor and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz-type inequality. By means of well-known contraction mapping and the Brouwer fixed-point theorem, we provide new results on the existence and uniqueness of solutions. In this work by use of the Caputo fractional difference operator and Hyer–Ulam stability definitions we check the sufficient conditions and solution of the equations to be stable, while most researchers have examined the necessary conditions in different ways. Further, we also establish some results regarding Hyers–Ulam, generalized Hyers–Ulam, Hyers–Ulam–Rassias, and generalized Hyers–Ulam–Rassias stability for our discrete fractional-order thermostat models. To support the theoretical results, we present suitable examples describing the thermostat models that are illustrated by graphical representation.
TL;DR: In this paper , a novel control method based on the use of Artificial Neural Networks to optimize the operations of refrigeration systems equipped with a fixed-speed compressor is proposed, which uses an Artificial Neural Network, which stem from a three-step process, able to provide the ON/OFF control loop with the optimal hysteresis value accordingly to the requirement of the user.
TL;DR: In this paper , the authors analyzed high resolution temperature measurements from a commercial building using wireless sensors to assess the performance and health of the building's HVAC zoning and controls system, and conducted two cluster analyses to evaluate the efficiency of the existing zoning structure and to find the optimal number of clusters.
Abstract: An efficient building should be able to control its internal temperature in a manner that considers both the building's energy efficiency and the comfort level of its occupants. Thermostats help to control the temperature within a building by providing real-time data on the temperature inside that space to determine whether it is within the acceptable range of that building's control system, and proper thermostat placement helps to better control a building's temperature. More thermostats can provide better control of a building, as well as a better understanding of the building's temperature distribution. In order to determine the minimum number of thermostats required to accurately measure and control the internal temperature distribution of a building, it is necessary to find the locations that show similar environmental conditions. In this paper, we analyzed high resolution temperature measurements from a commercial building using wireless sensors to assess the performance and health of the building's HVAC zoning and controls system. Then we conducted two cluster analyses to evaluate the efficiency of the existing zoning structure and to find the optimal number of clusters. K-means and time series clustering were used to identify the temperature clusters per building floor. Based on statistical assessments, we observed that time series clustering showed better results than k-means clustering.
TL;DR: In this article , an experimental setup was established for not only the averages of the effects of different thermal strategies established as commonly done in the literature, but also the changes during the whole warm up process were analyzed.
TL;DR: In this paper , a second-order fractional difference equation with one sensor and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz type inequality is proposed.
Abstract: Abstract A thermostat model described by a second-order fractional difference equation is proposed in this paper with one sensor and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz-type inequality. By means of well-known contraction mapping and the Brouwer fixed-point theorem, we provide new results on the existence and uniqueness of solutions. In this work by use of the Caputo fractional difference operator and Hyer–Ulam stability definitions we check the sufficient conditions and solution of the equations to be stable, while most researchers have examined the necessary conditions in different ways. Further, we also establish some results regarding Hyers–Ulam, generalized Hyers–Ulam, Hyers–Ulam–Rassias, and generalized Hyers–Ulam–Rassias stability for our discrete fractional-order thermostat models. To support the theoretical results, we present suitable examples describing the thermostat models that are illustrated by graphical representation.