Short-term forecasting of building cooling load based on data integrity judgment and feature transfer

Abstract

The building load forecasting with data-driven technologies guides the operation of the energy system. For new buildings or existing buildings with inadequate monitoring systems, transfer learning can be used to improve load forecasting accuracy. However, without an information integrity judgment, inappropriate migration features and incorrect transfer learning models will reduce the effectiveness of transfer learning. To bridge the gap, a data integrity judgment method is proposed in this paper to determine whether building features are absent. A max-relevance min-redundancy feature selection method with diffusion kernel density estimation (DKDE-mRMR) is established to select the key migrated features. With the transfer component analysis method used as the transfer learning model, the load data of three office buildings are taken as minimum feature set, source domain and target domain respectively for case study. The results prove that the CV-RMSE of load forecasting is over 41.0% by using insufficient information, whereas it can be reduced by 21.6% when information migration is deployed. Even if the target building after feature transfer is further missing the electricity consumption information for cooling load forecast, the CV-RMSE can still be maintained below 21.1% with good robustness.