CN111046536B - Thermal comfort modeling method based on changed adaptability factors and predicted average voting values - Google Patents
Thermal comfort modeling method based on changed adaptability factors and predicted average voting values Download PDFInfo
- Publication number
- CN111046536B CN111046536B CN201911167044.2A CN201911167044A CN111046536B CN 111046536 B CN111046536 B CN 111046536B CN 201911167044 A CN201911167044 A CN 201911167044A CN 111046536 B CN111046536 B CN 111046536B
- Authority
- CN
- China
- Prior art keywords
- thermal
- pmv
- thermal comfort
- tsv
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000006978 adaptation Effects 0.000 claims abstract description 47
- 230000035807 sensation Effects 0.000 claims abstract description 13
- 230000007613 environmental effect Effects 0.000 claims abstract description 12
- 230000003044 adaptive effect Effects 0.000 abstract description 17
- 241000724291 Tobacco streak virus Species 0.000 description 37
- 241000122133 Panicum mosaic virus Species 0.000 description 33
- 230000000694 effects Effects 0.000 description 6
- 238000004378 air conditioning Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 2
- 230000037323 metabolic rate Effects 0.000 description 2
- 238000000053 physical method Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000009423 ventilation Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
Landscapes
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a thermal comfort modeling method based on a changed adaptability factor and a predicted average ballot value, which comprises the following steps of:constructing a thermal comfort model naPMV, wherein λ is an adaptive factor that varies linearly with the inverse of the ambient temperature T:wherein p and q are constant parameters, and are calculated according to the following formula:PMV in i 、TSV i And T i The i-th thermal sensation forecast average vote value, the thermal sensation actual average vote value and the environmental temperature are collected in the field study respectively; collecting n PMV, TSV and environmental temperature T data sets;PMV respectively i 、TSV i The predicted average vote value and the actual average vote value of the heat sensation are all greater than zero after pretreatment. The invention combines the adaptability method and the reasoning method, introduces the changed adaptability factor into the reasoning method model PMV, thereby constructing a new thermal comfort model naPMV. The naPMV model improves the ability of the model PMV to interpret thermal adaptation, enabling more accurate, more reliable predictions of thermal comfort.
Description
Technical Field
The invention relates to the field of indoor thermal comfort prediction, in particular to a thermal comfort modeling method based on a changed adaptability factor and a predicted average vote value.
Background
Thermal comfort is one of the important indicators for evaluating the quality of indoor environments, and affects the health and working efficiency of indoor personnel. Thermal adaptation is defined as the phenomenon of the human body gradually decreasing in response to repeated thermal environmental stimuli, including the physiological, psychological and behavioral aspects. When the heat environment changes to cause uncomfortable heat, the heat adaptation is helpful for the human body to recover heat comfort.
The thermal adaptation can widen the thermal comfort zone, thereby contributing to energy saving. Thermal adaptation is able to raise the upper limit of acceptable room temperature (Operative temperature) from about 27 ℃ to 32 ℃ as specified by thermal comfort standard ASHRAE 55. The prior researches show that the energy consumption of the air conditioner can be reduced by about 5-10% when the indoor temperature is increased by 1 degree. Therefore, the thermal comfort model considering thermal adaptation is a guarantee for developing thermal comfort and low energy consumption buildings.
Currently, thermal comfort modeling methods for people can be divided into adaptive methods (adaptive approach) and inference methods (rational approach).
The adaptive method establishes a thermal comfort model based on field study, and aims to analyze the thermal comfort state in the real environment. The adaptation method can take into account the effect of thermal adaptation on thermal comfort. The adaptive approach relates thermal comfort linearly to ambient temperature (indoor or outdoor temperature). The slope of the linear model characterizes the extent of thermal adaptation, and the slope and intercept together characterize thermal adaptation under the influence of various factors, such as the cultural background, climate zone and building type. Thus, thermal comfort under thermal adaptation is dynamically variable with ambient temperature. However, the obvious disadvantage of the adaptive method is that it ignores the important effects of human body heat balance related factors such as indoor air velocity, humidity and human body metabolic rate on thermal comfort.
The reasoning method realizes the thermal comfort prediction by correlating the human body thermal balance with the thermal regulation. The predicted average vote value (Predicted Mean Vote-PMV) is a widely adopted thermal comfort model established based on an inference method, and is currently adopted by domestic and foreign thermal comfort standards, such as national standard GB/T50785, international standard ISO 7730, american standard ASHARE 55, european standard EN 15251 and the like. The inputs to the PMV include four environmental parameters (air temperature, air velocity, radiation temperature and humidity) and two personnel parameters (metabolic rate and garment thermal resistance). Thus, the inference method can take into account the effect of more parameters on thermal comfort than the adaptive method. However, the reasoning method is established based on environmental bin experiments, and thermal adaptation cannot be fully explained.
To expand the ability of reasoning methods to interpret thermal adaptations, university of Chongqing Yao Run teaches that a thermal comfort model aPMV is proposed. Yao Run teaches that based on negative feedback control theory, an adaptation factor is introduced into the PMV model to account for thermal adaptation caused by social, economic and cultural backgrounds, thermal history, thermal expectations, etc., to form an aPMV. Currently, the aPMV model has been adopted by national standard GB/T50785-2012.
However, the adaptation factor of an aPMV is a constant value for a given climate zone and building type, indicating that an aPMV assumes that thermal adaptation is static. But in a real-world environment, thermal adaptation is dynamic. Therefore, the existing thermal comfort model acmv cannot fully explain thermal adaptation, i.e. cannot consider the dynamic nature of thermal adaptation, so that the dynamic thermal comfort under the action of dynamic thermal adaptation cannot be accurately predicted.
Disclosure of Invention
The invention aims to provide a thermal comfort modeling method based on a changed adaptability factor and a predicted average vote value, so that the advantages of the adaptability method and the reasoning method are integrated, the reasoning method can comprehensively explain thermal adaptation (namely the dynamic property of the thermal adaptation), and further the thermal comfort prediction performance is improved.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the thermal comfort modeling method based on the changed adaptability factor and the predicted average voting value is based on a Fanger predicted average voting value PMV model, and a new thermal comfort model naPMV is constructed according to the following formula:
where λ is an adaptive factor that varies linearly with the inverse of the ambient temperature T:
where q and p are constant parameters.
The constant parameters q and p are calculated according to the following formulas:
in the formula, PMV i 、TSV i And T i The i-th thermal sensation forecast average vote value, the thermal sensation actual average vote value and the environmental temperature are collected in the field study respectively; collecting n PMV, TSV and environmental temperature T data sets;PMV respectively i 、TSV i The predicted average vote value and the actual average vote value of the heat sensation are all greater than zero after pretreatment.
Yao Run teaches that the developed aPMV is formed by introducing an adaptation factor lambda into the PMV as shown in equation 1. The adaptation factor λ is the ratio of the thermal adaptation K to the thermal environmental stimulus δ as shown in equation 2. However, the adaptive factor λ of the aPMV developed by the professor yao is a constant value and cannot explain the dynamics of thermal adaptation. According to the adaptation method, the present invention relates thermally adaptation K to ambient temperature T linearly as shown in equation 3. The thermal environmental stimulus δ may be an ambient temperature T as shown in equation 4. The invention proposes a dynamically changing adaptability factor by synthesizing formulas 2-4, namely, the adaptability factor is linearly related with the inverse of the ambient temperature T:the dynamic adaptation factor can account for the dynamics of thermal adaptation as a function of ambient temperature. The ambient temperature T may be an indoor temperature, such as an air temperature or an operating temperature (operative temperature), or an outdoor temperature, such as a month average outdoor air temperature or an outdoor average operating temperature (running mean outdoor temperature).
K＝pT+q (3)
δ＝T (4)
In the formulaWherein q and p need to be determined according to data collected in field study, the process is as follows:
the collected thermal sensation prediction average vote value PMV and the actual thermal sensation average vote value TSV are first preprocessed to values greater than zero. The pretreatment is to eliminate the negative impact of PMV and TSV sign inconsistencies on model performance. Since the absolute values of PMV and TSV are generally less than 5, the pretreatment of PMV and TSV is shown in equations 5 and 6. The preprocessing of the newly developed model naPMV is equation 7. The pre-treated naPMV can thus be expressed as formula 8. Then the objective function(shown in equation 9) to determine q and p. Equation 9 can be converted to equation 10. Because of +.>Trend towards 1, so minimizing the objective function is essentially minimizing the pre-processed naPMV and post-pre-processingIs a gap between TSVs. Combining equation 8 and equation 10, objective function +.>Can be further converted into formula 11. In order to minimize the objective function->The derivatives of equation 11 for q and p should both be 0, from which the respective formulas for q and p can be derived (as described above).
After the constant parameters q and p are calculated, the following formula is adopted:a new thermal comfort model naPMV can be constructed.
Compared with the prior art, the invention has the following beneficial effects:
the invention is based on a predictive average voting value PMV model of a Fanger, and adds an adaptive factor lambda which can linearly change along with the reciprocal of an ambient temperature T, thereby constructing an adaptive thermal comfort model naPMV with the changed adaptive factor lambda. The adaptive factor of the existing adaptive thermal comfort model aPMV is fixed, and the dynamic nature of thermal adaptation cannot be explained; therefore, compared with the prior art, the thermal comfort model naPMV constructed by the invention and based on the changed adaptability factor and the predicted average vote value can explain the dynamic nature of thermal adaptation, so that thermal comfort can be predicted more accurately.
Drawings
FIG. 1 is a schematic flow chart of the present invention for constructing a thermal comfort model.
Fig. 2 is a graph showing the thermal comfort prediction effect of the thermal comfort models PMV of the fixed adaptation factor thermal comfort models acmv and Fanger developed by teaching the thermal comfort models naPMV and Yao Run newly developed in case 1 of the present invention.
Fig. 3 is a graph showing the thermal comfort prediction effect of the thermal comfort models PMV of the fixed adaptation factor thermal comfort models acmv and Fanger developed by teaching the thermal comfort models naPMV and Yao Run newly developed in case 2 of the present invention.
Detailed Description
The invention will be further illustrated by the following description and examples, which include but are not limited to the following examples.
Examples
The invention provides a novel thermal comfort model which can fully explain thermal adaptation so as to more accurately predict thermal comfort. The method for constructing the thermal comfort model comprises three steps (shown in figure 1): firstly, preprocessing collected PMV, TSV and environmental temperature T data sets so that the PMV and the TSV are larger than zero (as shown in formulas 12 and 13); second step, using the collected ambient temperature T and the pre-processedCalculating constant parameters q and p according to formulas 14 and 15, respectively; thirdly, using the obtained constant parameters q and p, constructing a thermal comfort model naPMV according to a formula 16.
The advantages of the invention are illustrated in the following two cases. The case will use the mean absolute error MAE and the standard error SD to teach the thermal comfort prediction effect of the thermal comfort model PMV of the developed fixed adaptation factor thermal comfort model acmv and Fanger in comparison to the newly developed thermal comfort model naPMV, yao Run. The average absolute error MAE is used for evaluating the accuracy of the thermal comfort prediction model and is calculated according to a formula 17; the smaller the MAE, the higher the accuracy of the thermal comfort predictive model. The error standard deviation SD is used for evaluating the reliability of the thermal comfort prediction model and is calculated according to a formula 18; the smaller the SD, the higher the reliability of the thermal comfort predictive model.
Error i ＝Prediction i -TSV i (19)
Wherein Error is an Error, calculated according to equation 19, wherein the Prediction may be naPMV, aPMV or PMV; subscript i refers to the ith data set, and n total data sets.
Case 1
The PMV, TSV, and ambient temperature T data set in case 1 was from a natural draft building, which was five teaching buildings at Chongqing university. PMV is calculated and obtained based on a formula of physical measurement and ringer; TSVs are subjective heat sensation votes from college students; the ambient temperature T refers to the indoor air temperature. The aPMV developed in accordance with the teaching of PMV and TSV calculations Yao Run was obtained. Specific details regarding PMVs, TSVs, ambient temperature T, and aPMV are taught by reference Yao Run, et al: yao R, li B, liu J.2009.A theoretical adaptive model of thermal comfort-Adaptive Predicted Mean Vote (aPMV) Building and Environment,44 (10), 2089-2096.
Fig. 2 shows that both winter and summer, the aPMV is closer to the TSV than the PMV, i.e., the aPMV can predict the TSV better. The mean absolute error MAE of the PMV predicted TSVs is 0.59 and the standard error SD is 0.55. The aPMV reduces the mean absolute error MAE to 0.33 and the standard error SD to 0.31. However, fig. 2 also clearly shows the problem of the aPMV that the distance of the aPMV from the TSV increases with the increase of the TSV in summer. This illustrates that a fixed adaptation factor lacks the ability to account for the dynamics of thermal adaptation. The constant parameters q and p, calculated from equations 14 and 15, for the varying adaptability factor to vary linearly with the inverse of ambient temperature T, are-0.684 and 0.036, respectively, based on PMV, TSV, and ambient temperature T. Using the q and p obtained, a newly developed thermal comfort model napmV of the present invention is calculated according to equation 16. The varying adaptation factor enables the newly developed naPMV model to account for the dynamics of thermal adaptation, thereby improving thermal comfort prediction performance. The average absolute error MAE of the naPMV predicted TSV is 0.16, and the error standard deviation SD is 0.16; that is, compared with aPMV, the naPMV further improves the accuracy of the predicted TSV by 49.6 percent and the reliability of the predicted TSV by 46.6 percent.
Therefore, in a natural ventilation building, the thermal comfort model naPMV constructed according to the method of the present invention effectively improves the accuracy and reliability of thermal comfort prediction.
Case 2
In case 2, the PMV, TSV ambient temperature T data set is used for air conditioning buildings. The air-conditioning building is an office building of korea head. PMV is calculated and obtained based on a formula of physical measurement and ringer; TSVs come from subjective heat sensation votes; the ambient temperature T refers to the indoor air temperature. An aPMV developed in accordance with Yao Run teaching was calculated from the PMV and TSV data sets. For specific details on PMVs, TSVs, ambient temperature T and aPMV, reference is made to Kim et al: kim JT, lim JH, chokh, yun gy.2015.Development of the adaptive PMV model for improving prediction performance.
Fig. 3 shows that both winter and summer, the aPMV can predict TSV better than PMV. The mean absolute error MAE of the PMV predicted TSVs is 1.03 and the standard error SD is 0.70. The aPMV reduces the mean absolute error MAE to 0.23 and the standard error SD to 0.27. However, fig. 3 also clearly shows the problem of the acpmv, that is, in winter, the distance of the acpmv from the TSV increases as the TSV decreases; the distance of the aPMV from the TSV increases with increasing TSV in summer. This illustrates that a fixed adaptation factor lacks the ability to account for the dynamics of thermal adaptation. From the PMV, TSV, and ambient temperature T, constant parameters q and p (equations 14 and 15) calculated from equations 14 and 15 to vary the adaptability factor linearly with the inverse of ambient temperature T are-5.326 and 0.240, respectively. Using the q and p obtained, a newly developed thermal comfort model naPMV is calculated according to equation 16. The varying adaptation factor enables the newly developed model naPMV to account for the dynamics of thermal adaptation, thereby improving thermal comfort prediction performance. The average absolute error MAE of the naPMV predicted TSV is 0.17, and the error standard deviation SD is 0.12; that is, compared with aPMV, the naPMV further improves the accuracy of predicting TSV by 24.8%, and improves the reliability of predicting TSV by 56.6%.
Therefore, in an air-conditioning building, the thermal comfort model naPMV constructed according to the method of the invention effectively improves the accuracy and reliability of thermal comfort prediction.
In summary, the invention combines the adaptive method and the reasoning method to construct a new thermal comfort model naPMV; the thermal comfort model can fully interpret thermal adaptation, both in natural ventilation and in air conditioning, to more accurately predict thermal comfort. Compared with the prior art, the invention has obvious technical progress and outstanding substantive characteristics and obvious progress.
The above embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or color changes made in the main design concept and spirit of the present invention are still consistent with the present invention, and all the technical problems to be solved are included in the scope of the present invention.
Claims (1)
1. The thermal comfort modeling method based on the changed adaptability factor and the predicted average voting value is based on a Fanger predicted average voting value PMV model, and a thermal comfort model naPMV is constructed according to the following formula:
wherein λ is an adaptation factor; the method is characterized in that the adaptability factor lambda is linearly changed along with the reciprocal of the ambient temperature T:
wherein q and p are constant parameters; the constant parameters q and p are calculated according to the following formulas:
in the formula, PMV i 、TSV i And T i The i-th thermal sensation forecast average vote value, the thermal sensation actual average vote value and the environmental temperature are collected in the field study respectively; collecting n PMV, TSV and environmental temperature T data sets;PMV respectively i 、TSV i The predicted average vote value and the actual average vote value of the heat sensation are all greater than zero after pretreatment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911167044.2A CN111046536B (en) | 2019-11-25 | 2019-11-25 | Thermal comfort modeling method based on changed adaptability factors and predicted average voting values |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911167044.2A CN111046536B (en) | 2019-11-25 | 2019-11-25 | Thermal comfort modeling method based on changed adaptability factors and predicted average voting values |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111046536A CN111046536A (en) | 2020-04-21 |
| CN111046536B true CN111046536B (en) | 2023-11-14 |
Family
ID=70233304
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911167044.2A Active CN111046536B (en) | 2019-11-25 | 2019-11-25 | Thermal comfort modeling method based on changed adaptability factors and predicted average voting values |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111046536B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111503857B (en) * | 2020-05-06 | 2021-03-26 | 杭州晶一智能科技有限公司 | Environment temperature measuring method based on voting rapid filtering algorithm |
| CN113627448A (en) * | 2020-05-07 | 2021-11-09 | 香港大学浙江科学技术研究院 | Method for constructing human body thermal comfort prediction model, prediction method and system |
| CN111503858B (en) * | 2020-05-08 | 2021-03-26 | 杭州晶一智能科技有限公司 | Method for accurately measuring ambient temperature based on two-time voting filtering algorithm |
| CN112395732B (en) * | 2020-06-12 | 2024-04-02 | 香港城市大学深圳研究院 | Thermal comfort prediction method and device for enhancing thermal neutral adaptability |
| CN112066524B (en) * | 2020-08-02 | 2022-05-13 | 国网辽宁省电力有限公司电力科学研究院 | Non-residential building demand flexibility quantification method based on electric heat storage and comfort |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680025A (en) * | 2012-06-06 | 2012-09-19 | 安徽农业大学 | Indoor thermal comfort evaluation system |
| CN106052754A (en) * | 2016-05-18 | 2016-10-26 | 上海交通大学 | Heat comfortable instrument based on heat characteristics of Chinese people and evaluation method |
| CN107220417A (en) * | 2017-05-09 | 2017-09-29 | 清华大学 | CFD and human thermal response model coupled system and coupling process |
| CN108763730A (en) * | 2018-05-24 | 2018-11-06 | 浙江农林大学 | Shade tree screening technique, system, terminal and medium based on thermal comfort index |
| CN109738966A (en) * | 2019-01-07 | 2019-05-10 | 西安科技大学 | A kind of real-time forecasting system of hot comfort based on Studies of Human Body Heat Physiological effect model |
| CN110298128A (en) * | 2019-07-04 | 2019-10-01 | 香港城市大学成都研究院 | A kind of adaptive thermal comfort prediction model construction method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7206728B2 (en) * | 2002-09-25 | 2007-04-17 | Asahi Glass Company, Limited | Method for evaluating thermal comfort of a structure and an assisting method, program or system for designing a structure in consideration of thermal comfort |
| US8577650B2 (en) * | 2008-02-26 | 2013-11-05 | Kimberly-Clark Worldwide, Inc. | User interface for modeling thermal comfort |
-
2019
- 2019-11-25 CN CN201911167044.2A patent/CN111046536B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680025A (en) * | 2012-06-06 | 2012-09-19 | 安徽农业大学 | Indoor thermal comfort evaluation system |
| CN106052754A (en) * | 2016-05-18 | 2016-10-26 | 上海交通大学 | Heat comfortable instrument based on heat characteristics of Chinese people and evaluation method |
| CN107220417A (en) * | 2017-05-09 | 2017-09-29 | 清华大学 | CFD and human thermal response model coupled system and coupling process |
| CN108763730A (en) * | 2018-05-24 | 2018-11-06 | 浙江农林大学 | Shade tree screening technique, system, terminal and medium based on thermal comfort index |
| CN109738966A (en) * | 2019-01-07 | 2019-05-10 | 西安科技大学 | A kind of real-time forecasting system of hot comfort based on Studies of Human Body Heat Physiological effect model |
| CN110298128A (en) * | 2019-07-04 | 2019-10-01 | 香港城市大学成都研究院 | A kind of adaptive thermal comfort prediction model construction method |
Non-Patent Citations (2)
| Title |
|---|
| 室内热舒适评价方法探讨;纪秀玲;戴自祝;;建筑热能通风空调;第24卷(第05期);全文 * |
| 纪秀玲 ; 戴自祝 ; .室内热舒适评价方法探讨.建筑热能通风空调.2005,24(05),101-105. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111046536A (en) | 2020-04-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111046536B (en) | Thermal comfort modeling method based on changed adaptability factors and predicted average voting values | |
| Tewari et al. | Field study on indoor thermal comfort of office buildings using evaporative cooling in the composite climate of India | |
| Kumar et al. | Thermal comfort assessment and characteristics of occupant's behaviour in naturally ventilated buildings in composite climate of India | |
| Li et al. | An introduction to the Chinese Evaluation Standard for the indoor thermal environment | |
| Homod et al. | Gradient auto-tuned Takagi–Sugeno Fuzzy Forward control of a HVAC system using predicted mean vote index | |
| CN111079264B (en) | Thermal comfort modeling method based on expansion factors and predicted average voting values | |
| CN110298128B (en) | Construction method of adaptive thermal comfort prediction model | |
| CN114484557B (en) | Building group heat supply load regulation and control method based on target energy consumption management and control | |
| Kumar et al. | Field investigation on occupant's thermal comfort and preferences in naturally ventilated multi-storey hostel buildings over two seasons in India | |
| Méndez et al. | Empowering saving energy at home through serious games on thermostat interfaces | |
| Su et al. | Evaluation method of natural ventilation system based on thermal comfort in China | |
| Wang et al. | The virtual in-situ calibration of various physical sensors in air handling units | |
| Chen et al. | An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting | |
| TWI746087B (en) | Air conditioning system control method | |
| CN116007129B (en) | Warm ventilation air conditioner fault diagnosis method integrating human thermal comfort and detection data | |
| Schweiker et al. | Study on the effect of preference of air–conditioning usage on the exergy consumption pattern within a built environment | |
| Zhou et al. | Study on the relationship between thermal comfort and air-conditioning energy consumption in different cities | |
| Li et al. | Economic model predictive control in buildings based on piecewise linear approximation of predicted mean vote index | |
| Wang et al. | A model-based control of CO2 concentration in multi-zone ACB air-conditioning systems | |
| Kotisuryam et al. | Investigating HVAC thermal comfort and energy efficiency in commercial buildings | |
| Chen et al. | Model Free Adaptive Control for Air-Conditioning System in Office Buildings Based on Improved NSGA-II Algorithm | |
| Zhang et al. | Simulation and optimization analysis of summer indoor thermal environment for school buildings in hot summer and cold winter zone | |
| López-Pérez et al. | Energy Savings Comparative in Tropical Climate Educational Building by Adaptive Thermal Comfort Approach and Artificial Intelligence | |
| Ariffin | Thermal comfort and energy solutions for a better residential environment in Malaysia | |
| Zaharieva et al. | A Modified Approach for Forecasting Relative Humidity in Indoor Premises |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |