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Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning

Author

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  • Tetsushi Ono

    (Interdisciplinary Graduate School of Science Engineering (IGSES), Kyushu University Kasuga-koen 6-1, Kasuga-shi, Fukuoka 816-8580, Japan)

  • Aya Hagishima

    (Interdisciplinary Graduate School of Science Engineering (IGSES), Kyushu University Kasuga-koen 6-1, Kasuga-shi, Fukuoka 816-8580, Japan)

  • Jun Tanimoto

    (Interdisciplinary Graduate School of Science Engineering (IGSES), Kyushu University Kasuga-koen 6-1, Kasuga-shi, Fukuoka 816-8580, Japan)

Abstract

Understanding occupants’ behaviours (OBs) of heating and cooling use in dwellings is essential for effectively promoting occupants’ behavioural change for energy saving and achieving efficient demand response operation. Thus, intensive research has been conducted on data collection, statistical analysis, and modelling of OBs. However, the majority of smart metres currently deployed worldwide monitor only the total household consumption rather than appliance-level load. Therefore, estimating the turn-on/off state of specific home appliances from the measured household total electricity referred to as non-intrusive load monitoring (NILM), has gained research attention. However, the current NILM methods overlook the specific features of inverter-controlled heat pumps (IHPs) used for space heating/cooling; thus, they are unsuitable for detecting OBs. This study presents a rule-based method for identifying the occupants’ intended operation states of IHPs based on a statistical analysis of load data monitored at 423 dwellings. This method detects the state of IHPs by subtracting the power of sequential-operation appliances other than IHPs from the total household power. Three time-series characteristics, including the durations of power-on/off states and power differences between power-off/on states, were used for this purpose. The performance of the proposed method was validated, indicating an F-score of 0.834.

Suggested Citation

  • Tetsushi Ono & Aya Hagishima & Jun Tanimoto, 2022. "Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14863-:d:969100
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    References listed on IDEAS

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