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Financiaciado para publicación en acceso aberto: Universidade de Vigo/CISUG Providing accurate information about the indoor environmental quality (IEQ) conditions inside building spaces is essential to assess the comfort levels of their occupants. These values may vary inside the same space, especially for large zones, requiring many sensors to produce a fine-grained representation of the space conditions, which increases hardware installation and maintenance costs. However, sound interpolation techniques may produce accurate values with fewer input points, reducing the number of sensors needed. This work presents a platform to automate this accurate IEQ representation based on a few sensor devices placed across a large building space. A case study is presented in a research centre in Spain using 8 wall-mounted devices and an additional moving device to train a machine learning model. The system yields accurate results for estimations at positions and times never seen before by the trained model, with relative errors between 4% and 10% for the analysed variables. Ministerio de Ciencia, Innovación y Universidades | Ref. RTI2018-096296-B-C2 Ministerio de Ciencia, Innovación y Universidades | Ref. FPU17/ 01834 Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/01187 Universidad de Vigo | Ref. 00VI 131H 641.02 |
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