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.
A platform for a flexible, smart sensing system using available hardware components for monitoring the operation of a greenhouse is presented. The smart sensor is based on a ZigBee MCU embedded system with multiple connectivity options to facilitate digital or analogue sensors as well as the necessary peripherals for energy management and programming/debugging. A number of physical parameters may be simultaneously monitored by each node, such as temperature, relative humidity, CO2, light intensity, soil pH / moisture through appropriate sensors. Basic functions, such as sensor differential detection and measurement consistency may be performed at the smart sensor. A central node, also acting as the Zigbee network coordinator will concentrate the various measurements through the wireless network, act as a local display and also forward the information to a back-end. The back-end will provide proper measurement visualization (including history) through any web-enabled device, as well as services such as alert notification in hazardous situations (e.g. flood / heating failure).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.