This paper presents a dynamic model of a variable speed heat pump (VSHP) in a commercial building that responds to direct load control (DLC) signals, updated every 4 s, for the improvement of grid ...frequency regulation (GFR). The model is simplified for real-time simulation studies with the time horizon ranging from seconds to hours, but still sufficiently comprehensive to analyze the operational characteristics such as the heat rate and coefficient of performance. A variable speed drive-controlled induction motor model is also established for the adjustment of the VSHP input power. A dynamic model of an experimental room is then developed to estimate the effect of the DLC application to the VSHP on its indoor air temperature for two different cooling systems. Furthermore, small signal analysis is performed to evaluate both the transient response of the DLC-enabled VSHP and its contribution to GFR. Finally, with an isolated microgrid implemented with Matlab/Simulink, simulation studies demonstrate that the VSHP can be effectively exploited as the DLC-enabled load while still ensuring building occupant comfort and long-term device performance.
The determination of the indoor air temperature is necessary for evaluating human comfort, health, and living conditions. Existing measuring methods require entering a room, which can disturb the ...daily lives of residents and consume large amounts of manpower, material, and financial resources. To overcome these obstacles, an exploratory approach was proposed in this study to estimate the indoor air temperature by obtaining the outdoor building window surface temperature without intrusion using infrared technology. A numerical model was established to describe the heat transfer process between the indoor and outdoor air via window glass. Experiments were conducted in a test room to capture infrared images of the exterior window and measure indoor air temperatures and window surface temperatures under different modes. The estimated indoor air temperatures were compared with the experimental data. The effects of window property parameters and ambient parameters on indoor air temperature estimation were analyzed. Results show that the deviations of the indoor air temperature between estimated and measured values in heating, natural ventilation, and cooling modes varied from −0.7 °C to 0.6 °C, −1.1 °C–0.7 °C, and −0.1 °C–1.3 °C, respectively. Based on the sensitivity analysis, the outer surface temperature of the window outer layer was crucial for estimating the accuracy of the indoor air temperature in practical applications. The proposed exploratory approach provides a potential means for remotely obtaining indoor air temperatures using infrared technology.
•An exploratory approach is proposed to remotely estimate indoor air temperature.•A numerical model is established to describe the heat transfer process.•The estimated indoor air temperatures are compared with the measured data.•The effects of different parameters on indoor air temperature are analyzed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Exploring the link between thermal comfort and cortisol, a stress hormone, at different air temperatures can give a theoretical basis for creating a comfortable and healthy indoor environment, ...although the relationship is not yet obvious. We considered three temperatures and two exposure levels, collected the subjects' subjective evaluations and saliva, detected the concentration of cortisol in saliva, and analyzed the change characteristics of subjective evaluations and cortisol concentration at different temperatures with exposure time and their relationships at different exposure times. Subjective evaluations were better and cortisol concentration was lower at neutral temperature, according to our findings. Furthermore, there was a U-shaped distribution of cortisol concentration with thermal sensation vote (TSV), and cortisol concentration decreased with increasing thermal comfort vote (TCV). In comparison to “hot” and “cold,” the concentration of cortisol corresponding to “neutral” after 30 min of exposure declined by 179.0% and 59.3%, respectively; the concentration for “very uncomfortable” was higher by 191.4% than for “comfortable.” Following a 60-min exposure, the concentrations that corresponded to “neutral” and “very uncomfortable” were, respectively, lower by 89.3% and 143.0% than “hot” and “comfortable.” Our findings suggest that cortisol derived from human saliva may be a novel and useful objective indicator assessing human thermal comfort. This study can deepen our understanding of the relationship between human thermal comfort and stress hormones, and relevant conclusions provide theoretical basis for building indoor thermal environment.
•Air temperature significantly affected the human salivary cortisol concentration.•The more comfortable the environment, the lower the cortisol concentration.•Exposure time affected the relations between cortisol concentration and TSV and TCV.•Salivary cortisol may be a new and useful index assessing human thermal comfort.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The prediction for indoor temperature as an essential reference for system tuning has attracted great concern in the HVAC field. The existing researches mostly focused on transplanting novel machine ...learning algorithms, especially recurrent neural network (RNN), from the neuro-linguistic programming (NLP) like text generation and speech recognition into HVAC systems. But unlike semantics, the large-scale data processing during the training phase requires the high-speed processor and large memory, which restricts their online application in HVAC systems. In this work, we develop a strategy of improving gated RNN prediction for indoor temperature by estimating time delays of HVAC systems. The suggested strategy can lighten the computational burden and decrease memory capacity to favor online prediction and control, where the traceback range of error is narrowed down to the nearest-neighbors delay periods and the parameter learning time is reduced to about 2% of the original. Among them, we introduce transfer entropy (TE) into the HVAC field to estimate its time delays by mining monitoring data. Compared with correlation coefficients, it can filter redundant information between variables and discern the nonlinearity. Subordinately, we reconstruct time series by embedding time delays as inputs of the prediction model, and clip gradients within backpropagation through time (BPTT) with multivariate TE index, which promote the correlation between inputs and output and absorb the most valuable information in the parameter learning. Lastly, the proposed methods are validated with simulated and real time series. This work is enlightening and instructive to improving time series prediction in HVAC systems.
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•A strategy of improving indoor temperature prediction in HVAC system is developed.•A model-free method is introduced into the HVAC field to estimate its time delays.•A method of promoting correlation between time-series data by delay time is given.•A gradient clipping strategy in the parameter learning based on TE is present.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Generally, studies where the zonal indoor air temperature is measured only consider the manufacturers' sensor accuracy to determine the overall Temperature Uncertainty (UT), without considering other ...sources of uncertainties that affect the indoor air temperature measurement of a thermal zone within a building. Thus, in this research, the definition and estimation method of the overall Temperature Uncertainty (UT) has been developed, together with a decoupling method to obtain the Temperature's Spatial Uncertainty (UTSP), in which all causes of uncertainty regarding the temperature measurement are included, except the Temperature Sensor Uncertainty (UTS). Using the measurements of the indoor air temperature from sensors installed randomly in four offices of an in-use tertiary building, the developed statistical analysis has been applied to estimate and decouple the overall indoor air Temperature Uncertainty (UT) of each monitored office. Each studied thermal zone represents a different office typology composed of divisions with different volumes that allow their classification.
The first study carried out was the estimation of the experimental accuracy by estimating the Temperature Sensor Uncertainty (UTS), including both the sensor uncertainty plus the monitoring system uncertainty in this value. This first study has allowed us to discover the importance of the estimation of the Temperature Sensor Uncertainty (UTS) when installing a monitoring system, inasmuch as the experimental accuracy could be different from the manufacturer's accuracy, as reflected in the results of this research.
Secondly, based on the developed statistical method, for each of the monitored thermal zones, the overall zonal indoor air Temperature Uncertainty (UT) is estimated in order to finally decouple it into the Temperature Sensor Uncertainty (UTS) and Temperature's Spatial Uncertainty (UTSP). The decoupling results show that, depending on the office typology, the percentage weight of the Temperature Sensor Uncertainty (UTS) only represents between 5% and 11% of the overall zonal indoor air Temperature Uncertainty (UT).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Natural ventilation is an important resource to cool down indoor areas at sub/tropical areas. However, within the high density urban context, anthropogenic heat from mechanical cooling system could ...have significant impact on the thermal environment at naturally ventilated indoor areas. This study focuses on residential neighbourhood and aims to develop evidence-based strategies to improve residential building design to mitigate the negative impact of anthropogenic heat. A parametric study was conducted using validated Computational Fluid Dynamics (CFD) models to investigate the impact of various design parameters, e.g., building typology, floor elevation, apartment location, and heat emission source location on the indoor air temperature increment (ΔT). First, the parametric study indicates that ΔT is generally lower at high-rise residential buildings than slab residential buildings. Secondly, lower ΔT at slab buildings with courtyards does not means better indoor thermal comfort because lower ΔT is caused by poor natural ventilation. Thirdly, the apartments located at the windward side of courtyard could have much higher ΔT than the ones at leeward side. When organizing hybrid ventilation for the buildings with courtyards, the ventilation direction is important to prevent heat entering the indoor areas from courtyard. Fourthly, significant increment of ΔT with increasing floor elevation is observed at slab buildings. Last but not least, changing heat source locations from the windward to leeward side could not help to decrease ΔT, but putting the heat sources at the façade parallel to the incoming wind could be very helpful. The above research outputs enable architects and planners to assess the current heat risk and take the necessary actions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
We present an approach to generate location-specific forecasts of indoor temperature (Ti) and thermal comfort and issue indoor heat warnings for occupational settings. Indoor forecasts are generated ...using standard outdoor weather forecasting products and an artificial neural network (ANN) trained on-site using local indoor measurements from a low-cost sensor system measuring Ti and indoor physiologically equivalent temperature (PETi). The outcomes are hourly indoor Ti and PETi forecasts. Different ANN-based forecast products using different predictors were concurrently tested at 121 workplaces in agricultural, industrial, storage, and office buildings using data for an entire annual cycle. A forecast was considered skillful when the Ti and PETi forecast was <2 K from actual measurements. The best-performing model used the predictors time of year, week, and day; solar position; and outdoor weather forecast variables to train and run an ANN to predict Ti or PETi. It had an annual average mean absolute forecast error of 0.87 K for Ti and 0.99 K for PETi over the next 24 h, with Pearson correlation coefficients of 0.98 and 0.97, respectively. Overall, 91% of Ti forecasts and 88% of PETi forecasts were skillful. Indoor forecasts showed larger errors in the summer than in the winter. We conclude that combining indoor data with weather forecasts using ANNs could be implemented widely to provide location-specific indoor weather forecasts to improve and localize heat and health warning systems.
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•Neural networks predict indoor temperatures, thermal comfort and heat warnings.•Neural networks trained using indoor sensor data and outdoor weather forecasts.•Workflow concurrently tested at 121 typical workplaces over full annual cycle.•Best model predicts indoor temperature with <1 K error, 92% of all forecasts <2 K.•Approach enables skilful and location-specific indoor heat and health warnings.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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