Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings

Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency

Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics

Linking expansion behaviour of extruded potato starch/rapeseed press cake blends to rheological and technofunctional properties

In order to valorise food by-products into healthy and sustainable products, extrusion technology can be used. Thereby, a high expansion rate is often a targeted product property. Rapeseed press cake (RPC) is a protein- and fibre-rich side product of oil pressing. Although there is detailed knowledge about the expansion mechanism of starch, only a few studies describe the influence of press cake addition on the expansion and the physical quality of the extruded products. This study assessed the effect of RPC inclusion on the physical and technofunctional properties of starch-containing directly expanded products. The effect of starch type (native and waxy), RPC level (10, 40, 70 g/100 g), extrusion moisture content (24, 29 g/100 g) and barrel temperature (20–140 °C) on expansion, hardness, water absorption, and solubility of the extrudates and extruder response was evaluated. At temperatures above 120 °C, 70 g/100 g of RPC increased the sectional and volumetric expansion of extrudates, irrespective of starch type. Since expansion correlates with the rheological properties of the melt, RPC and RPC/starch blends were investigated pre- and postextrusion in a closed cavity rheometer at extrusion-like conditions. It was shown that with increasing RPC level the complex viscosity |ƞ*| of extruded starch/RPC blends increased, which could be linked to expansion behaviour

A narrative review of heavy metals in cosmetics; health risks

Cosmetics products since the dawn of civilization are considered a part of routine body care. The last few decades these products have had increasing and applied to the human body for beautification. Xenobiotics and heavy metals including chromium, copper, iron, mercury, cadmium, arsenic and nickel, classified as a light metal, are determinate in various types of cosmetics such as color cosmetics, face and body care products, hair cosmetics, herbal cosmetics. In cosmetic products was harmful when they occur in excessive amounts. Evidence studies determinate that in commercially available cosmetics toxic metals might present in amounts creating a danger to human health. The aim of this review is to assess identification of elimination, sources and control of sources, and monitoring countries marketed exposures and hazards can be used to prevent heavy metals toxicity. © 2019, Advanced Scientific Research. All rights reserved

Infection and Transovarial Transmission of Rickettsiae in Dermacentor variabilis Acquired by Artificial Feeding

In this study we examined the efficiency of an in vitro feeding technique using glass microcapillaries as a method of establishing rickettsiae-infected lines of ticks. To quantify the volume ingested by ticks during microcapillary feeding, the incorporation of radiolabeled amino acids in tick gut and hemolymph was calculated. Fifteen of 18 ticks consumed between 0.06 μl and 6.77μl. However, ingestion of fluid was not correlated to weight gain during capillary feeding. Uninfected and partially fed laboratory-reared female Dermacentor variabilis ticks were exposed to either Rickettsia montana- or Rickettsia rhipicephali-infected Vero cells via microcapillary tubes, returned to rabbit hosts, and allowed to feed to repletion. All tissues collected from ticks allowed to feed overnight on rickettsiae-infected fluids were found to be infected when examined by IFA. When rickettsiae-infected and uninfected capillary-fed ticks were allowed to feed to repletion and lay eggs, no significant differences in mean engorgement weight or fecundity was observed. When we assessed the efficiency of transovarial transmission of rickettsiae by ticks that imbibed rickettsiae-infected cells by polymerase chain reaction (PCR) and IFA, infection was detected by PCR in the eggs from 85% of the ticks exposed to R. montana and 69% of the ticks exposed to R. rhipicephali. Rickettsial genes were not amplified in samples of the uninfected controls. Examination by IFA of egg samples from females exposed to rickettsiae-infected cells identified rickettsiae in 100% of the samples tested, while the uninfected controls were negative