A global base temperature dataset for building energy demand modeling

Accurate building energy demand modeling is critical to decarbonizing regional energy systems. The cooling and heating degree-day models are widely used due to their simplicity and low data requirements; however, the lack of accurate base temperature data limits their performance. In particular, the scarcity of high temporal resolution building energy demand data constrains regional-scale base temperature estimation through conventional methods such as the energy signature method and the performance line method. To address this limitation, this study develops a global regional-scale base temperature dataset based on the BiLSTM neural network framework with an attention mechanism. The dataset includes both cooling base temperature (<i>Tcool</i>) and heating base temperature (<i>Theat</i>) for each region, defined at a spatial scale equivalent to a U.S. state or a Chinese province.