Financiaciado para publicación en acceso aberto: Universidade de Vigo/CISUGProviding 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-C2Ministerio de Ciencia, Innovación y Universidades | Ref. FPU17/ 01834Ministerio de Ciencia, Innovación y Universidades | Ref. FPU19/01187Universidad de Vigo | Ref. 00VI 131H 641.0
