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

Author

Listed:
  • José Joaquín Aguilera

    (International Centre for Indoor Environment and Energy, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

  • Rune Korsholm Andersen

    (International Centre for Indoor Environment and Energy, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

  • Jørn Toftum

    (International Centre for Indoor Environment and Energy, Technical University of Denmark, 2800 Kongens Lyngby, Denmark)

Abstract

Non-optimal air temperatures can have serious consequences for human health and productivity. As the climate changes, heatwaves and cold streaks have become more frequent and intense. The ClimApp project aims to develop a smartphone App that provides individualised advice to cope with thermal stress outdoors and indoors. This paper presents a method to predict indoor air temperature to evaluate thermal indoor environments. Two types of input data were used to set up a predictive model: weather data obtained from online weather services and general building attributes to be provided by App users. The method provides discrete predictions of temperature through a decision tree classification algorithm. The data used to train and test the algorithm was obtained from field measurements in seven Danish households and from building simulations considering three different climate regions, ranging from temperate to hot and humid. The results show that the method had an accuracy of 92% (F1-score) when predicting temperatures under previously known conditions (e.g., same household, occupants and climate). However, the performance decreased to 30% under different climate conditions. The approach had the highest performance when predicting the most commonly observed indoor temperatures. The findings suggest that it is possible to develop a straightforward and fairly accurate method for indoor temperature estimation grounded on weather data and simple building attributes.

Suggested Citation

  • José Joaquín Aguilera & Rune Korsholm Andersen & Jørn Toftum, 2019. "Prediction of Indoor Air Temperature Using Weather Data and Simple Building Descriptors," IJERPH, MDPI, vol. 16(22), pages 1-20, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:22:p:4349-:d:284685
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    References listed on IDEAS

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    1. Kelly, Scott & Shipworth, Michelle & Shipworth, David & Gentry, Michael & Wright, Andrew & Pollitt, Michael & Crawford-Brown, Doug & Lomas, Kevin, 2013. "Predicting the diversity of internal temperatures from the English residential sector using panel methods," Applied Energy, Elsevier, vol. 102(C), pages 601-621.
    2. Liuhua Shi & Itai Kloog & Antonella Zanobetti & Pengfei Liu & Joel D. Schwartz, 2015. "Impacts of temperature and its variability on mortality in New England," Nature Climate Change, Nature, vol. 5(11), pages 988-991, November.
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    Cited by:

    1. Jakob Petersson & Kalev Kuklane & Chuansi Gao, 2019. "Is There a Need to Integrate Human Thermal Models with Weather Forecasts to Predict Thermal Stress?," IJERPH, MDPI, vol. 16(22), pages 1-18, November.
    2. B. R. M. Kingma & H. Steenhoff & J. Toftum & H. A. M. Daanen & M. A. Folkerts & N. Gerrett & C. Gao & K. Kuklane & J. Petersson & A. Halder & M. Zuurbier & S. W. Garland & L. Nybo, 2021. "ClimApp—Integrating Personal Factors with Weather Forecasts for Individualised Warning and Guidance on Thermal Stress," IJERPH, MDPI, vol. 18(21), pages 1-26, October.

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