Separation of Residential Space Cooling Usage From Smart Meter Data

• Published in: IEEE transactions on smart grid Vol. 11; no. 4; pp. 3107 - 3118
• Main Authors: Liang, Huishi, Ma, Jin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air conditioning; Cooling; Customers; Data models; demand response potential; Direct reduction; disaggregation; Energy consumption; Energy management; FHMM; Ground truth; Hidden Markov models; HVAC; Load modeling; Markov chains; smart meter data; Smart meters; Space cooling (buildings); targeting; Temperature distribution; Transition probabilities
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2020.2965958

For demand response (DR) programs that focus on the energy consumption of heating, ventilation, and air conditioning (HVAC), the knowledge on HVAC usage of individual customers is of great value for DR program implementation. This paper presents a novel methodology to extract space cooling usage from smart meter data. A multisequence, non-homogeneous Factorial Hidden Markov Model (MN-FHMM) is proposed to disaggregate the whole-house energy consumption into an HVAC component and a baseload (i.e., the sum of non-temperature-sensitive loads) component. Rather than holding a constant transition probability over the whole process, a time-varying transition probability model is developed to characterize the dynamic nature of the evolution of users' energy consumption. We also discuss how to use the disaggregation results to estimate the HVAC related DR potential for individual customers, which can further inform DR programs to target customers more cost-effectively. Data experiments on ground truth data validate both the accuracy and the robustness of the proposed model as well as the effectiveness of the targeting strategy based on the disaggregation results.