Abstract
In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.
Highlights
With the development of technology and the increasing usage of electrical appliances and automated services, the electric energy needs have been growing steadily for the last century with an annual growth of approximately 3.4% per year in the last decade [1]
The performance was evaluated in terms of power estimation accuracy (EACC), as proposed in [55] and defined in Equation (3)
The estimation accuracy is taking into account the estimated power pm and the ground-truth power consumption pm for each device m, where T is the number of frames and M is the number of disaggregated devices including the ghost power
Summary
With the development of technology and the increasing usage of electrical appliances and automated services, the electric energy needs have been growing steadily for the last century with an annual growth of approximately 3.4% per year in the last decade [1]. To achieve sustainable economic growth energy consumption in industrial and residential areas must be minimized under the consideration of rising volatility of nowadays energy production with increasing amounts of renewable energies [6]. Under the consideration of sustainable development several studies investigated real time pricing with additional storage systems [7,8] or large scale energy buffering [6] to reduce electrical energy consumption and peak loads. Other studies indicate that detailed analysis and real-time feedback of energy consumption in residential areas can lead to up to 20% savings in energy consumption through detection of faulty devices and poor operational strategies and would improve the sustainability of nowadays consumer households [9,10]. To make use of those techniques, accurate and fine-grained monitoring of electrical energy consumption is needed [14]
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