Symeonidis N., Nalmpantis C., Vrakas D. (2019) A Benchmark Framework to Evaluate Energy Disaggregation Solutions. In: Macintyre J., Iliadis L., Maglogiannis I., Jayne C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham
Author(s): Symeonidis N., Nalmpantis C., Vrakas D.
Keywords: NILM, Energy disaggregation, Artificial neural networks, Stacked learning, Benchmark
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Abstract: Energy Disaggregation is the task of decomposing a single meter aggregate energy reading into its appliance level subcomponents. The recent growth of interest in this field has lead to development of many different techniques, among which Artificial Neural Networks have shown remarkable results. In this paper we propose a categorization of experiments that should serve as a benchmark, along with a baseline of results, to efficiently evaluate the most important aspects for this task. Furthermore, using this benchmark we investigate the application of Stacking on five popular ANNs. The models are compared on three metrics and show that Stacking can help improve or ensure performance in certain cases, especially on 2-state devices.