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Thermal comfort evaluations in the built environment are essential to occupant's satisfaction and also building energy consumption. Traditionally, thermal comfort has been assessed by Fanger's PMV model that was developed based on extensive laboratory tests. However, it's been found that PMV predictions are not good in field studies, especially in buildings with natural ventilation. In this paper, machine learning (ML) algorithms were used to predicted occupants' thermal comfort votes (TCV) and thermal sensation votes (TSV), using 5512 sets of thermal comfort data collected in naturally ventilated residential buildings in fourteen cities in China. Environmental parameters, personal parameters, climatic types, and adaptive control measures were considered and used as input parameters for the ML model. It was found that environmental parameters (both outdoors and indoors), personal parameters (metabolic rate and clothing insulation), and climatic types all significant affect both TCV and TSV, while adaptive control measures only affect TSV but not TCV. Comparing with established models (PMV, ePMV and aPMV), the ML models had smaller errors in predicting TSV and TCV. Based on these results, we suggest that the ML, especially ANNs model was reliable in predicting occupants' TCV and TSV in naturally ventilated residential buildings, and performed better than traditional thermal balance based models. Moreover, using the new model, we found that acceptable temperature ranges in naturally ventilated buildings were far wider than the ASHRAE adaptive comfort zone, suggesting that appropriate models should be established based on local data. [ABSTRACT FROM AUTHOR]

We developed a tree-ring width chronology from pine trees ( Pinus tabulaeformis and Pinus armandii) stand near the peaks of Huashan, Shaanxi, north-central China. Growth-climate response analyses showed that the radial growth of pine trees is mainly influenced by April-June precipitation. A model to reconstruct precipitation based on tree widths was constructed, accounting for 55 % of the instrumental variance during the period 1953-2012. Spatial correlation analyses between the reconstruction and observed gridded precipitation data shows that the seasonal precipitation reconstruction captures regional climatic variations over north China. Compared with the historical archives and other tree-ring records in north China, many large-scale drought events, linked to the El Niño-Southern Oscillation (ENSO), were found. Many of these events have had profound impacts on the people of north China over the past several centuries. Composite maps of sea surface temperatures and 500 hPa geopotential heights for selected extremely dry and wet years in Huashan show characteristics similar to those related to the ENSO patterns, particularly with regard to ocean and atmospheric conditions in the equatorial and north Pacific. Our 531-year precipitation reconstruction for Huashan provides a long-term perspective on current and 20th century wet and dry events in north China, and is useful to guide expectations of future variability, and helps us to address climate change. [ABSTRACT FROM AUTHOR]