Abstract
Load identification is an essential step in Non-Intrusive Load Monitoring (NILM), a process of estimating the power consumption of individual appliances using only whole-house aggregate consumption. Such estimates can help consumers and utility companies improve load management and save power. Current state-of-the-art methods for load identification generally use either steady state or transient features for load identification. We hypothesize that these are complementary features and so a hybrid combination of them will result in an improved appliance signature. We propose a novel hybrid combination that has the advantage of being low-dimensional and can thus be easily integrated with existing classification models to improve load identification. Our improved hybrid features are then used for building appliance identification models using Naive Bayes, KNN, Decision Tree and Random Forest classifiers. The proposed NILM methodology is evaluated for robustness in changing environments. An automated data collection setup is established to capture 7 home appliances aggregate data under varying voltages. Experimental results show that our proposed feature fusion based algorithms are more robust and outperform steady state and transient feature-based algorithms by at least +9% and +15% respectively.
Highlights
The global energy demand is rising and is a major cause for global warming and climate change
The U.S Energy Information Administration (EIA) projects that world energy consumption will grow by nearly 50% and the energy consumed in the buildings sector will increase by 65% by 2050 (EIA 2019)
Experimental results Experiments are designed to evaluate the effectiveness of our proposed feature fusion based Non-Intrusive Load Monitoring (NILM) classifiers
Summary
The global energy demand is rising and is a major cause for global warming and climate change. Improving energy efficiency and reducing energy consumption are two important sustainability measures. Detailed appliance specific energy usage feedback would enable consumers to reduce consumption by 5-15% (Darby and et al 2006). Load monitoring is a method of determining energy consumption and operating states of individual appliances. Intrusive Load Monitoring (ILM) monitors appliance consumption using a low-end energy sensor connected to an appliance. ILM can precisely monitor and control appliances but are not cost effective. NILM or load disaggregation is an approach to estimate individual appliance energy consumption using aggregate load measurement obtained from a single energy meter. NILM is economical (2020) 3:9 as it uses single energy meter for load monitoring. NILM research is gaining importance due to advancements in the area of AI, IoT, Smart meters and Smart grids (Ruano et al 2019)
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