Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors

Aguilera, José Joaquín; Andersen, Rune Korsholm; Toftum, Jørn

doi:10.3390/ijerph16224349

Cited by 22 publications

(17 citation statements)

References 35 publications

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“…To support the user in selecting an indoor temperature, a suggested indoor air temperature and the corresponding relative humidity are presented, yet only the values input by the user are used. The algorithm to determine indoor temperature is dominated by Mediterranean climates where indoor settings relied mostly on natural ventilation cooling to maintain the indoor temperature [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

“…For indoor conditions, air temperature is input by the user, and indoor mean radiant temperature is assumed to be equal to air temperature. To support a user in selecting the right indoor air temperature, a suggested indoor temperature is provided [ 26 ]. Based on the suggested indoor air temperature, the suggested relative humidity is calculated as the ratio of actual outdoor water vapour pressure and saturated indoor water vapour pressure:

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

ClimApp—Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress

Kingma

Steenhoff

Toftum

et al. 2021

IJERPH

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This paper describes the functional development of the ClimApp tool (available for free on iOS and Android devices), which combines current and 24 h weather forecasting with individual information to offer personalised guidance related to thermal exposure. Heat and cold stress assessments are based on ISO standards and thermal models where environmental settings and personal factors are integrated into the ClimApp index ranging from −4 (extremely cold) to +4 (extremely hot), while a range of −1 and +1 signifies low thermal stress. Advice for individuals or for groups is available, and the user can customise the model input according to their personal situation, including activity level, clothing, body characteristics, heat acclimatisation, indoor or outdoor situation, and geographical location. ClimApp output consists of a weather summary, a brief assessment of the thermal situation, and a thermal stress warning. Advice is provided via infographics and text depending on the user profile. ClimApp is available in 10 languages: English, Danish, Dutch, Swedish, Norwegian, Hellenic (Greek), Italian, German, Spanish and French. The tool also includes a research functionality providing a platform for worker and citizen science projects to collect individual data on physical thermal strain and the experienced thermal strain. The application may therefore improve the translation of heat and cold risk assessments and guidance for subpopulations. ClimApp provides the framework for personalising and downscaling weather reports, alerts and advice at the personal level, based on GPS location and adjustable input of individual factors.

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Section: Resultsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

ClimApp—Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress

Kingma

Steenhoff

Toftum

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…The information gain is applied to calculate the amount of an attribute, which contributes to estimating the classes. The entropy and information gain can be expressed by the following Equations (2) and (3) [ 11 , 29 , 30 ],

where p Ti is the proportion of data points; C T is the total number of classes; T i is the one sample among all the n subsets in which the total amount of training data T was divided due to an attribute X.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Arulmozhi

Basak

Sihalath

et al. 2021

Animals

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Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes.

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“…With more accurate indoor climate estimates, heat-health warning systems may provide more accurate alerts based on risk factors mentioned in this review. In order to get more accurate predictions, indoor air temperature, air velocity, humidity, and mean radiant temperature must be measured or estimated based on outdoor prevailing weather conditions and building characteristics for naturally ventilated buildings must be specified [92]. The importance of each of the thermal climate factors may be different in indoor environments compared to outdoor environments.…”

Section: Human Thermal Models and Indicesmentioning

confidence: 99%

Is There a Need to Integrate Human Thermal Models with Weather Forecasts to Predict Thermal Stress?

Petersson

Kuklane

Gao

2019

IJERPH

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More and more people will experience thermal stress in the future as the global temperature is increasing at an alarming rate and the risk for extreme weather events is growing. The increased exposure to extreme weather events poses a challenge for societies around the world. This literature review investigates the feasibility of making advanced human thermal models in connection with meteorological data publicly available for more versatile practices and a wider population. By providing society and individuals with personalized heat and cold stress warnings, coping advice and educational purposes, the risks of thermal stress can effectively be reduced. One interesting approach is to use weather station data as input for the wet bulb globe temperature heat stress index, human heat balance models, and wind chill index to assess heat and cold stress. This review explores the advantages and challenges of this approach for the ongoing EU project ClimApp where more advanced models may provide society with warnings on an individual basis for different thermal environments such as tropical heat or polar cold. The biggest challenges identified are properly assessing mean radiant temperature, microclimate weather data availability, integration and continuity of different thermal models, and further model validation for vulnerable groups.

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Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors

Cited by 22 publications

References 35 publications

ClimApp—Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress

ClimApp—Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Is There a Need to Integrate Human Thermal Models with Weather Forecasts to Predict Thermal Stress?

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