HVAC load Disaggregation using Low-resolution Smart Meter Data

Traditional non-intrusive load monitoring (NILM) methods are effective for load disaggregation using high resolution smart meter data collected by power quality meters. However, smart meter data collected and stored by utilities are normally 15-, 30- or 60-minute in granularity, making most NILM methods ineffective. This paper presents a novel sequential energy disaggregation (SED) algorithm for extracting heating, ventilation, and air conditioning (HVAC) energy consumptions from residential and small commercial building loads using 30 min smart meter data. Large, infrequently-used loads are first detected and removed from the total building energy consumption. Then, base energy consumption curves, defined as the energy consumption without heating and cooling loads, are identified using the mild-day method. After that, the heating and cooling loads are extracted using an average value subtracting method. Simulation results with real data show that the proposed SED method is computationally efficient, simple to implement and robust in performance across different types of buildings.