All-sky longwave radiation modelling based on infrared images and machine learning

Sky longwave radiation is an important input parameter for heat transfer and radiant energy balance calculations of the building envelope, and it has a significant effect on the accuracy of predictions of the energy consumption of buildings. The ability of the sky to emit longwave radiation needs to be accurately quantified under different weather conditions. In this study, a low-cost sky cloudiness observation platform based on an infrared imager was established, and an algorithm for calculating the cloudiness of the sky dome by stitching and processing infrared images was proposed. A machine learning model was developed by collecting local meteorological data; these data were then input into the synthetic minority oversampling technique algorithm. This model can es-timate sky cloudiness based on conventional meteorological parameters. Nine different machine learning algo-rithms were tested, and a cloudiness prediction model based on the XGBoost algorithm was finally established, which had a prediction accuracy of 88.81%. Furthermore, an all-sky longwave radiation model was developed by introducing cloudiness parameters. The applicability of different longwave radiation models for clear and cloudy skies in subtropical climates was examined, and the coefficient values of the different models were modified. Finally, the formula applicable to subtropical climates was determined via a fitting method.