A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm

Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network (Bi-LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory (LSTM) network and recurrent neural network (RNN), Bi-LSTM has the advantages of stronger recognition ability. Finally, precision (P), recall (R), accuracy (A), and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances

Smart Demand Response Based on Smart Homes

Smart homes (SHs) are crucial parts for demand response management (DRM) of smart grid (SG). The aim of SHs based demand response (DR) is to provide a flexible two-way energy feedback whilst (or shortly after) the consumption occurs. It can potentially persuade end-users to achieve energy saving and cooperate with the electricity producer or supplier to maintain balance between the electricity supply and demand through the method of peak shaving and valley filling. However, existing solutions are challenged by the lack of consideration between the wide application of fiber power cable to the home (FPCTTH) and related users’ behaviors. Based on the new network infrastructure, the design and development of smart DR systems based on SHs are related with not only functionalities as security, convenience, and comfort, but also energy savings. A new multirouting protocol based on Kruskal’s algorithm is designed for the reliability and safety of the SHs distribution network. The benefits of FPCTTH-based SHs are summarized at the end of the paper

Sliding-impact bistable triboelectric nanogenerator for enhancing energy harvesting from low-frequency intrawell oscillation

A sliding-mode bistable triboelectric nanogenerator (SBTENG) has already been proven to be highly efficient for harvesting energy from low-frequency vibration. However, a SBTENG would undergo low-amplitude intrawell oscillation and thus output low power, if the excitation is weak. To enhance the efficiency of harvesting energy from low-frequency intrawell oscillation, a novel sliding-impact bistable TENG (SIBTENG) is proposed. The sliding-mode component of the SIBTENG enables energy harvesting from interwell oscillation effectively, while the impact-mode structure plays a vital role in enhancing energy harvesting from intrawell oscillation. The equation of motion of the SIBTENG is derived using Hamilton's principle and then numerically solved to obtain the dynamic responses. Subsequently, the output performance of the SIBTENG is evaluated by solving the electrical equation, which is unidirectional coupled to the equation of motion. Finally, experiments on the prototype of the SIBTENG are conducted to verify this design concept, which indicates good consistency between the theoretical and experimental results. Importantly, the impact-mode structure can notably enhance energy harvesting from intrawell oscillation. The output power of the devised SIBTENG is improved by about 100% over the SBTENG when they experience intrawell oscillation. The SIBTENG thereby enables high-efficiency energy harvesting whatever the oscillation pattern is

Characterization of Novel Integrons, In1085 and In1086, and the Surrounding Genes in Plasmids from Enterobacteriaceae, and the Role for attCaadA16 Structural Features during attI1 × attC Integration

Novel class 1 integrons In1085 and In1086, containing the class D β-lactamase -encoding gene blaOXA, were identified in clinical enterobacterial strains. In this study, we aimed to characterize the genetic contexts of In1085 and In1086, with the goal of identifying putative mechanisms of integron mobilization. Four plasmids, approximately 5.3, 5.3, 5.7, and 6.6 kb, from 71 clinical Enterobacteriaceae strains were found to contain class 1 integrons (In37, In62, In1085, and In1086, respectively). Two of these plasmids, pEco336 and pNsa292, containing In1085 and In1086, respectively, were further characterized by antibiotic susceptibility testing, conjugation experiments, PCR, sequencing, and gene mapping. The OXA-type carbapenemase activities of the parental strains were also assessed. The results revealed that the novel integrons had different genetic environments, and therefore demonstrated diverse biochemical characteristics. Using evolutionary inferences based on the recombination of gene cassettes, we also identified a role for attCaadA16 structural features during attI1 × attC insertion reactions. Our analysis showed that gene cassette insertions in the bottom strand of attCaadA16 in the correct orientation lead to the expression the encoded genes from the Pc promoter. Our study suggests that the genetic features harbored within the integrons are inserted in a discernable pattern, involving the stepwise and parallel evolution of class 1 integron variations under antibiotic selection pressures in a clinical setting

Additional file 1: of Genetic characterization of novel class 1 Integrons In0, In1069 and In1287 to In1290, and the inference of In1069-associated integron evolution in Enterobacteriaceae

Sequence analysis for In0, In1069, In893, and In1287 to In1290, respetively. (ZIP 65 kb

Additional file 1: of Characterization of the novel In1059 harbouring VIM gene cassette

Analysis for integrons: In1059, In843, In846, In37 and In62. (XLS 48 kb