Prediction of building power consumption using transfer learning-based reference building and simulation dataset

Abstract

With the advancements in data processing technologies and the increased use of renewable energy systems, the development of microgrid has gained attention. Consequently, a method for machine learning studies has increased for rational building power consumption. Extensive historical data of building power consumption are required for a high accuracy prediction; however, in practice, it is difficult to gather such data from existing or new buildings. Therefore, this study proposed a method for using transfer learning based on the simulation dataset of a reference building. The transfer learning long short-term memory (TL-LSTM) model developed in this study trained only on 24 h of office building power consumption data and predicted after 24 h. The accuracy of TL-LSTM was evaluated using various simulation and experimental data, and the factors affecting the performance of TL-LSTM (the number of training data and climate zone) were analyzed. Consequently, compared to the long short-term memory (LSTM) model, the TL-LSTM model demonstrated a higher accuracy with an average coefficient of variation of the root mean square error (CVRMSE) of 4.25% and mean bias error (MBE) of 1.70%. Furthermore, the prediction for the next 24 h was possible when at least 22 training data points were gathered. Finally, when the climate zone was the same for the target and source datasets, high accuracy was demonstrated even if the location of each building was different. Additionally, the source dataset could be replaced with a simulation dataset.