A new deep learning-based approach for predicting the geothermal heat pump’s thermal power of a real bioclimatic house

Abstract In recent years, growing concern about climate change and the need to reduce greenhouse gas emissions have highlighted the role of energy efficiency and sustainability on the global agenda. Energy policies are decisive in establishing regulatory frameworks and incentives to address these challenges, leading to an inclusive and more resilient energy transition. In this context, geothermal energy is an essential source of renewable, low-emission energy, capable of providing heat and electricity sustainably. The present research focuses on a bioclimatic house’s geothermal energy system based on a heating pump and a horizontal heat exchanger. The main aim is to predict the generated thermal power of the heat pump using historical data from several sensors. In particular, two approaches were proposed with both uni-variate and multi-variate scenarios. Several deep learning techniques were applied: LSTM, GRU, 1D-CNN, CNN-LSTM, and CNN-GRU, obtaining satisfactory results over the whole dataset, which comprised one year of data acquisition. Specifically, promising results have been achieved using hybrid methods combining recurrent-based and convolutional neural networks.