  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.
