Transfer Learning Integrating Similarity Analysis for Short-Term and Long-Term Building Energy Consumption Prediction

Abstract

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack in operational data due to relatively limited monitoring systems, causing the failure of using data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed method can improve the Coefficient of Variation of the Root-Mean-Square-Error (CV-RMSE) between 70.0% and 81.3%, while for the multi-step prediction, the improvement is between 26.8% and 65.5%. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan.

Keywords: Building energy consumption predictions, Transfer Learning, deep learning, Similarity analysis, Transformer Model