Data-driven adaptive GM(1,1) time series prediction model for thermal comfort

In this paper, the future prediction of predicted mean vote (PMV) index of indoor environment is studied. PMV is the evaluation index used in this paper to represent the thermal comfort of human body. According to the literature, the main environmental factors affecting PMV index are temperature, humidity, black globe temperature, wind speed, average radiation temperature, and clothing surface temperature, and there is a complex nonlinear relationship between the six variables. Due to the coupling relationship between the six parameters, the PMV formula can be simplified under specific conditions, reducing the monitoring of variables that are difficult to observe. Then, the improved grey system prediction model GM(1,1) with optimized selection dimension is used to predict the future time of PMV. Due to the irregularity, uncertainty and fluctuation of PMV values in time series, based on the original GM(1,1) time series prediction, an adaptive GM(1,1) improved model is proposed, which can continuously change with time series and enhance its prediction accuracy. By contrast, the improved GM(1,1) model can be derived from the sliding window of the adaptive model through changes in the dataset and get better model grades. It lays a foundation for the future research on the predicted index of PMV, so as to set and control the air conditioning system in advance, to meet the intelligence of modern intelligent home and humanized function of sensing human comfort.

Automated summary

This paper studies the future prediction of predicted mean vote (PMV) index of indoor environment. PMV is used as the evaluation index to represent the thermal comfort of the human body. The main environmental factors affecting PMV index are identified as temperature, humidity, black globe temperature, wind speed, average radiation temperature, and clothing surface temperature, which have a complex nonlinear relationship. An improved grey system prediction model GM(1,1) is used to predict the future time of PMV. Additionally, an adaptive GM(1,1) improved model is proposed to enhance the prediction accuracy based on the irregularity and uncertainty of PMV values in time series.