Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning

Abstract

A wide range of techniques exist to help control the thermal comfort of an occupant in indoor environments. A novel technique is presented here to adaptively estimate the occupant’s metabolic rate. This is performed by utilising occupant’s actions using computer vision system to identify the activity of an occupant. Recognized actions are then translated into metabolic rates. The widely used Predicted Mean Vote (PMV) thermal comfort index is computed using the adaptivey estimated metabolic rate value. The PMV is then used as an input to a fuzzy control system. The performance of the proposed system is evaluated using simulations of various activities. The integration of PMV thermal comfort index and action recognition system gives the opportunity to adaptively control occupant’s thermal comfort without the need to attach a sensor on an occupant all the time. The obtained results are compared with the results for the case of using one or two fixed metabolic rates. The included results appear to show improved performance, even in the presence of errors in the action recognition system.

Application of machine learning in thermal comfort studies: A review of methods, performance and challenges

“…Seven papers ( [18], [25], [37], [38], [39], [40], [41]) studied all seasons and one paper [42] mentioned that a period of 10 months was investigated. On the other hand, some studies ( [30], [43], [44], [45], [39], [46], [47], [48], [49], [50]) did not clarify their studied seasons. Kim et al…”

A Review of Thermal Comfort in Primary Schools and Future Challenges in Machine Learning Based Prediction for Children

“…TPV and PMV are the next most popular output/label, and TCV is also considered in a few studies [87,98,107,130]. Other output parameters include energy consumption [131], occupant behavior [93,132], etc.…”

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

“…In the world of modern business, it is required to improve the performance and the quality of fuzzy systems when they are used to predict and control real-time nonlinear dynamical industrial processes. Among others, the processes of financial systems [1][2][3][4][5], industrial manufacturing processes [6][7][8], autonomous mobile robots [9][10][11][12][13], intelligent controllers [14][15][16][17][18][19][20][21][22][23][24][25][26], route selection [27,28], clustering systems [29,30], medical systems [31][32][33], vision and pattern recognition systems [34][35][36], granular computing and optimization [37,38], database and information systems [39,40], and plant monitoring and diagnostics [18,[41][42][43][44] are characterized by high uncertainty, nonlinearity, and time-varying behavior …”