期刊
IEEE TRANSACTIONS ON SMART GRID
卷 11, 期 3, 页码 2652-2661出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2019.2959770
关键词
Hidden Markov model; hidden semi-Markov model; load modeling; residential appliances; short-term load forecast
资金
- National Science Foundation (NSF) [ECCS-1554178]
- Department of Energy (DOE) Solar Energy Technologies Office (SETO) [DE-EE00031003]
- DOE Department of Energy (ARPA-E) [DE-AR0000697]
- Stanford Graduate Fellowship
The expansion of residential demand side management programs and increased deployment of controllable loads 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. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. We also propose an algorithm for short-term load forecasting as a key application for appliance-level load models. Case studies performed using granular sub-metered power measurements from various types of appliances demonstrate the effectiveness of the proposed load model for short-term prediction.
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