In China’s hot summer and cold winter (HSCW) area, historical policies have resulted in the absence of central heating. However, intensifying extreme weather and rising living standards recently have led to a surge in heating energy consumption, accompanied by substantial carbon emissions. This study explores the feasibility of achieving zero-carbon heating (ZCH) in the HSCW area, specifically deriving heating energy from electricity generated by photovoltaic (PV) systems. This paper first measures indoor temperature, energy consumption data, and simulates PV generation across 20 residential areas in two typical cities in the HSCW area, Wuhan and Shanghai. Then, sensitivity analysis and machine learning regression with Shapley additive explanations are conducted between 14 morphology parameters and 3 primary energy indicators: ratio of energy consumption to indoor-outdoor temperature difference, available qualified PV generation for heating energy consumption, and ratio of qualified surface area. Subsequently, the study employed multi-objective optimization to balance the energy indicators of three residential area prototypes: tower, slab, and courtyard. Analysis of measured data reveals that the openness index has the most significant influence on heating energy consumption, while facade area has the greatest impact on PV indicators. However, the trend in morphological parameters optimized for the ZCH objective varies depending on building type and plot size. Conclusively, all residential types can realize ZCH, with achievable proportions at 61.00% of slab-style, 37.30% of courtyard-style, and 18.35% of tower-style. This research proposes novel approaches for reducing carbon emissions in the HSCW high-density settlements, thus providing references for related cases.
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