Building electrical load forecasting with occupancy data based on wireless sensing

Building electrical load forecasting, as a necessary foundation for building energy management, is of great significance for building energy efficiency and sustainable urban development. However, the accuracy of forecasting can hardly be guaranteed due to the stochastic nature of occupant behavior. To overcome this challenge, this paper proposes a data fusion-based building electrical load forecasting method with occupancy data obtained by wireless sensing technology. Firstly, a wireless sensing scheme is developed, which utilizes pre-existing wireless devices within the building energy management system (BEMS), offering a cost-effective means of obtaining occupancy information without violating occupant privacy. Moreover, to estimate the pattern of occupant behavior in the entire building, an improved stacked sparse auto-encoder (ISSAE) model is developed, which involves unsupervised feature fusion from information sources of varying significance. Finally, to cope with the time-varying and strongly fluctuating building load, a multi-source data fusion forecasting model based on the ensemble deep random vector functional link (edRVFL) is proposed. This model integrates the contributions of the latest accuracy and diversity through the ranking-based dynamic integration strategy. The effectiveness of the proposed method is validated in a commercial building. The experimental results demonstrate that, compared with the load forecasting scheme without occupancy information, the proposed method can improve the forecasting accuracy on RMSE and MAPE by 13.21% and 14.97%, respectively, while cost-effectiveness and privacy are ensured.