Abstract
The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.
Highlights
Easy to use and efficient energy disaggregation tools may be the holy grail of energy efficiency [1]
Our results show that by using a richer set of features that are generated by the smart meter itself—and no additional computation is needed—
By light-weight, we mean a streaming algorithm that can be executed on the low memory and low processing power CPU on the smart meter
Summary
Easy to use and efficient energy disaggregation tools may be the holy grail of energy efficiency [1]. The commercial impact of energy disaggregation at the level of the home customers is the increased utility customer engagement and the reduced energy usage The goal at this level is to itemize the consumer’s energy bill [2], analyze the energy usage and cost per household appliance, make personalized and prioritized energy savings recommendations or even go as far as identify faulty appliances (e.g., frosting cycle of a fridge with a damaged seal is more frequent than a normal one [3]). All these should be viable through a single sensor per household that monitors the total energy consumption and other related quantities [4]. With respect to the consumer, his/her energy behavior is affected by raising inefficiencies that fall under the following 3 categories:
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