Anomaly Detection in Small-Scale Industrial and Household Appliances

Anomaly detection is concerned with identifying rare events/ observations that differ substantially from the majority of the data. It is considered an important task in the energy sector to enable the identification of non-standard device conditions. The use of anomaly detection techniques in small-scale residential and industrial settings can provide useful insights about device health, maintenance requirements, and downtime, which in turn can lead to lower operating costs. There are numerous approaches for detecting anomalies in a range of application scenarios such as prescriptive appliance maintenance. This work reports on anomaly detection using a data set of fridge power consumption that operates on a near zero energy building scenario. We implement a variety of machine and deep learning algorithms and evaluate performances using multiple metrics. In the light of the present state of the art, the contribution of this work is the development of a inference pipeline that incorporates numerous methodologies and algorithms capable of producing high accuracy results for detecting appliance failures

Fully Hydrogenated Beryllium Nanoclusters

We present the ground state and energetically low structures of Be_<i>n</i>H_2<i>n</i> nanoclusters as predicted using density functional theory (DFT) and employing the M06 meta-hybrid exchange-correlation functional. Results using the M06 functional are benchmarked against high accuracy coupled-cluster CCSD­(T) and found to be in excellent agreement. For small values of <i>n</i>, the linear or polymeric form is the lowest energy geometry, while for sizes larger, <i>n</i> > 9 ring type and link type structures are the energetically lowest configurations. This trend has also been observed through ab initio molecular dynamics (AIMD) simulations at finite temperatures. In addition to the binding energies of the structures we report on polymerization energies, Be–H bond energies with respect to coordination details, hydrogen desorption energies of saturated and oversaturated species, as well as computed infrared spectra of all the ground state and energetically low lying structures presented. Furthermore, we find that the saturated polymeric forms of the nanoclusters <i>cannot</i> retain molecular hydrogen, in contrast to what is expected when zero point energy corrections are not taken into account