Residential Building Heating Load Prediction using Deep Learning TabNet Model

Efficient heating load (HL) predictions in residential buildings are vital in energy optimization and cost savings. Deep learning models with a high ability to solve complex tasks could be good tools for this purpose. Therefore, in this study, we proposed a new deep learning model for heating load prediction based on an attentive interpretable tabular learning model (TabNet). The in-hand dataset contains only 768 records which causes deep learning models&rsquo weak performance in comparison with classical machine learning tools. To solve the problem and utilize the ability of deep learning models, a new hybrid model that combines TabNet and a tabular data augmentation module based on GAN and CGAN methods has been proposed. The data augmentation module increased the size of the training dataset 5 times. The performance results indicated that the BiLSTM outperforms other well-known deep learning models without data augmentation, including ResNet, Fully Connected Neural Networks (FCNN), TabNet, And LSTM. By utilizing the data augmentation module the TabNet-GAN model outperformed other deep learning models and brought comparable results with other classical machine learning HL prediction models. Therefore, the TabNet-GAN model could be used for residential building HL prediction and help engineers select the best plan/design from the energy usage perspective.