2018/10/08 by Ji, Yuting, Buechler, Elizabeth, Rajagopal, Ram
#Applications (stat.AP) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.1810.03727
The expansion of residential demand response programs and increased deployment of controllable loads will require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand, and an algorithm for short-term load forecasting. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. Case studies performed using sub-metered 1-minute power consumption data from several types of appliances demonstrate the effectiveness of the model for load forecasting and anomaly detection.