Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

open access articleProviding the user with appliance-level consumption data is the core of each energy efficiency system. To that end, non-intrusive load monitoring is employed for extracting appliance specific consumption data at a low cost without the need of installing separate submeters for each electrical device. In this context, we propose in this paper a novel non-intrusive appliance recognition system based on (i) detecting events in the aggregated power signal using a novel and powerful scheme, (ii) applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and (iii) adopting an ensemble bagging tree classifier along with comparing its performance with various machine learning schemes. Moreover, to validate the proposed model, an empirical investigation is conducted on two real and public energy consumption datasets, namely, the GREEND and REDD, in which consumption readings are collected at low-frequencies. In addition, a comprehensive review of recent non-intrusive load monitoring approaches has been conducted and presented, in which their characteristics, performances and limitations are described. The proposed non-intrusive load monitoring system shows a high appliance recognition performance in terms of the accuracy, F1 score and low time complexity when it has been applied to different households from the GREEND and REDD repositories, in which every house includes various domestic appliances. Obtained results have described, e.g., that average accuracies of 97.01% and 96.36% have been reached on the GREEND and REDD datasets, respectively, which outperformed almost existing solutions considered in this framework

Deep Eutectic Solvents (DESs) and their applications [forthcoming]

Deep Eutectic Solvents (DESs) and Their Application

Gapless Spin Liquid Ground State in the S=1/2 Vanadium Oxyfluoride Kagome Antiferromagnet [NH4 ]2[C7H14N][V7O6F18]

The vanadium oxyfluoride [NH4]2[C7H14N][V7O6F18] (DQVOF) is a geometrically frustrated magnetic bilayer material. The structure consists of S=½ kagome planes of V4+ d1 ions with S=1 V3+ d2 ions located between the kagome layers. Muon spin relaxation measurements demonstrate the absence of spin freezing down to 40 mK despite an energy scale of 60 K for antiferromagnetic exchange interactions. From magnetization and heat capacity measurements we conclude that the S=1 spins of the interplane V3+ ions are weakly coupled to the kagome layers, such that DQVOF can be viewed as an experimental model for S=½ kagome physics, and that it displays a gapless spin liquid ground state.PostprintPeer reviewe

An ionothermally prepared S=1/2 vanadium oxyfluoride kagome lattice

Frustrated magnetic lattices offer the possibility of many exotic ground states that are of great fundamental importance. Of particular significance is the hunt for frustrated spin-1/2 networks as candidates for quantum spin liquids, which would have exciting and unusual magnetic properties at low temperatures. The few reported candidate materials have all been based on d9 ions. Here, we report the ionothermal synthesis of [NH4]2[C7H14N][V7O6F18], an inorganic-organic hybrid solid that contains a S = 1/2 kagome network of d1 V4+ ions. The compound exhibits a high degree of magnetic frustration, with significant antiferromagnetic interactions but no long-range magnetic order or spin-freezing above 2 K, and appears to be an excellent candidate for realizing a quantum spin liquid ground state in a spin-1/2 kagome network.PreprintPostprintPeer reviewe