MTL-Deep-STF: A Multitask Learning Based Deep Spatiotemporal Fusion Model for Outdoor Air Temperature Prediction in Building HVAC Systems

Buildings consume large quantities of energy. Reducing building energy consumption is essential to achieving carbon neutrality goals. Building energy consumption is strongly influenced by various meteorological factors for which outdoor air temperature is a proxy. This study proposes a data-driven multitask-learning deep spatiotemporal fusion model (MTL-Deep-STF) that predicts outdoor air temperature to provide baseline data against which improvements to conserve energy in heating, ventilation, and air conditioning (HVAC) systems can be proposed and assessed. MTL-Deep-STF is a generic multiple input-multiple output model that predicts temperatures for suc-cessive time points in separate high-level tasks. Each task uses a multilayer perceptron to combine meteorological data from neighboring observation stations. CNN-GRU is used to extract spatially local long-term and short-term temporal features that are shared between different tasks via CNN. Comparative validation experiments using real world datasets for complex terrains showed that the model was highly accurate in both single step and multistep predictions. One-step-ahead predictions for winter (MAE = 0.428 degrees C, RMSE = 0.705, Acc <= 2 degrees C = 97.832%) and summer (MAE = 0.467 degrees C, RMSE = 0.706, Acc <= 2 degrees C = 97.899%) datasets were particularly accurate. This accuracy was coupled with high robustness (R2 = 0.976 for winter and R2 = 0.981 for summer). Use of the model can conserve building energy.