Abstract
The power demand forecast for buildings can generate useful day-ahead predictions in power system planning and operation. However, the information in the forecast needs to be interpreted by a person with domain expertise. Moreover, automated interpretation of upcoming abnormal behaviors needs ground-truth labeling, but labels are not always available from power meter data. In this paper, we propose a novel Predictive Power Demand Analytics Methodology (PPDAM), based on deep neural networks and symbolic aggregate approximation, to predict the pattern profile of power demand in a building and upcoming normal (motif) and anomalous (discord) behaviors. The experimental results indicate that a power forecast could be mapped as different foreseeable demand patterns, each with a specific probability of occurrence. The reliability of anomaly prediction is evaluated by a classification test of which the accuracy is 88% and the F1 score is 87.38%. The outcomes of this work could provide building operators with a solution to derive latent information in power consumption data. The derived information could be used to improve the working conditions of the building’s power system.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call