Modeling energy-related CO2 emissions from office buildings using general regression neural network

Abstract Carbon dioxide (CO2) emissions from urban office buildings energy usages (BEC) constitute a substantial component of anthropogenic greenhouse gas emission, and are set to rapidly increase with further urbanization. Establishing a concise, accurate, and realistic model that can predict future emissions is challenging but essential for strategies to develop low carbon construction and sustainable development in urban areas. In this paper, the operational energy use for 294 office buildings across China was collected and analyzed. We focus on four main variables, and analyze a further ten second-level variables, to elucidate the role that a building’s occupants, its structural characteristics, and localized natural conditions play in determining energy consumption. Using general regression neural network (GRNN), the factors’ direct and indirect effects on energy consumption were tested. A building’s structural attributes had the most impact on energy-related CO2 emissions, followed by the relevant socioeconomic conditions, the micro-climate, and finally the regional climate. A version of the model that was constructed with interaction between the four main variables was found to be the most precise. GRNN combined with urban development scenarios was used for the prediction of cities’ future CO2 emissions. Economic development and improving standards in the construction industry could have significant impacts on future CO2 emissions. This study provides a detailed method that could be used to explore the dynamics of office energy use and competing options for the construction of low-carbon office buildings.

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