Benefits of Intelligent Computational Methods for Big Data Analysis on IEQ Research

The quality of the environment inside in buildings matters as much as the quality of the environment outdoors. Actually even more, as the health and comfort of the building occupants depend on it. Indoor air quality (IAQ) and thermal comfort conditions (ITQ) as well as hygrometric aspect are concerned to define indoor climate quality (ICQ) which is just part of Indoor Environmental Quality (IEQ). An enlarged concept of IEQ comprises four more quality aspects; sound (ISQ), lighting (ILQ), odor (IOQ), and vibration (IVQ). A high indoor environmental quality can increase health, wellbeing and productivity of building occupants, but also decrease costs for energy and building maintenance. This paper presents materials and methods used on indoor environmental research. In the studies of our research group measured parameters incorporate outdoor climate conditions, indoor thermal, hygrometric, and air quality aspects as well as energy consumption readings including electricity, district heating, and water. Data analysis were performed using computational, visualization and grouping artificial neural network methods. Furthermore, additional external study cases utilizing artificial neural networks (ANN), model-based control, and big data analysis are presented and compared. In this paper, in all 16 examples of intelligent data analysis and knowledge deployment are reviewed in the sense of the benefits of methods used.