Clustering residential electricity load curve via community detection in network

Performing analytics on the load curve(LC)of customers is the foundation for demand response which requires a better understanding of customers'consumption pattern(CP)by analyzing the load curve.However,the performances of previous widely-used LC clustering methods are poor in two folds:larger number of clusters,huge variances within a cluster(a CP is extracted from a cluster),bringing huge difficulty to understand the electricity consumption pattern of customers.In this paper,to improve the performance of LC clustering,a clustering framework incorporated with community detection is proposed.The framework includes three parts:network construction,community detection,and CP extraction.According to the cluster validity index(CVI),the integrated approach outperforms the previous state-of-the-art method with the same amount of clusters.And the approach needs fewer clusters to achieve the same performance measured by CVI.

A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data.Consequently,a method for cleaning wind power anomaly data by combining image processing with community detection algorithms(CWPAD-IPCDA)is proposed.To precisely identify and initially clean anomalous data,wind power curve(WPC)images are converted into graph structures,which employ the Louvain community recognition algorithm and graph-theoretic methods for community detection and segmentation.Furthermore,the mathematical morphology operation(MMO)determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning.The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines(WTs)in two wind farms in northwest China to validate its feasibility.A comparison was conducted using density-based spatial clustering of applications with noise(DBSCAN)algorithm,an improved isolation forest algorithm,and an image-based(IB)algorithm.The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms,achieving an approximately 7.23%higher average data cleaning rate.The mean value of the sum of the squared errors(SSE)of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms.Moreover,the mean of overall accuracy,as measured by the F1-score,exceeds that of the other methods by approximately 10.49%;this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Seasonal changes in the demersal nekton community off the Changjiang River estuary

The diversity,community structure and seasonal variation in demersal nekton off the Changjiang(Yangtze)River estuary was evaluated using monthly trawl survey data,collected between December 2008 and November 2009.A total of 95 species(56 teleosts,11 cephalopods,and 28 decapod crustaceans)from 69 genera,49 families and 15 orders were collected.These species could be classifi ed into six groups on the basis of temporal distribution patterns.The resident crab Ovalipes punctatus dominated the community,both in number and biomass.A clear seasonal succession was observed in the species composition.Cluster analysis revealed three primary seasonal groups corresponding to the samples collected in winter-spring,late spring-summer and late summer-autumn.The highest biomass and lowest diversity were observed in summer,while the lowest biomass and highest diversity in winter.The abundance-biomass comparison curves and community composition suggested that the investigated community was moderately disturbed.The results suggest that reduction in fi shing pressure and in the degree of seasonal hypoxia are essential for sustainable resource management off the Changjiang River estuary.

Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution attenuation.Spatial variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling strategies.Here,we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri,USA and examined the microbial community composition with 16S rRNA gene amplicon sequencing.Riverine microbiomes across the transect exhibited extremely high similarity,with Pearson’s correlation coefficients above 0.9 for all pairwise community composition comparisons.However,despite the high similarity,PERMANOVA revealed significant spatial differences between near-shore and nearcenter communities(p=0.001).Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity(R^(2)=0.89).Despite being primarily explained by neutral processes,LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center,indicating an additional role of environmental filtering.Notably,the local variations within a river transect can have profound impacts on the documentation of alpha diversity.Taxon-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level,yet four,six and seven samples were able to capture 80%of the phylum-level,family-level,and genus-level diversity,respectively.This study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms,demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.

Security and Privacy-Preserving Metering Service in the Smart Grid

The deployment of smart metering provides an immense amount of data for power grid operators and energy providers. By using this data, a more efficient and flexible power grid can be realized. However, this data also raises privacy concerns since it contains very sensitive information about customers. In this paper, we present a security and privacy-preserving metering scheme for the community customers, by utilizing the password authenticated key exchange (PAKE) protocol and Elliptic Curve Cryptosystem (ECC). The proposed scheme will protect the community network from possible malicious behavior, and security analysis is also given in the paper.

Species Abundance in a Forest Community in South China：A Case of Poisson Lognormal Distribution

: Case studies on Poisson lognormal distribution of species abundance have been rare, especially in forest communities. We propose a numerical method to fit the Poisson lognormal to the species abundance data at an evergreen mixed forest in the Dinghushan Biosphere Reserve, South China. Plants in the tree, shrub and herb layers in 25 quadrats of 20 m× 20 m, 5 m× 5 m, and 1 m× 1 m were surveyed. Results indicated that: (i) for each layer, the observed species abundance with a similarly small median, mode, and a variance larger than the mean was reverse J-shaped and followed well the zero-truncated Poisson lognormal; (ii) the coefficient of variation, skewness and kurtosis of abundance, and two Poisson lognormal parameters (& and μ) for shrub layer were closer to those for the herb layer than those for the tree layer; and (iii) from the tree to the shrub to the herb layer, the α and the coefficient of variation decreased, whereas diversity increased. We suggest that: (i) the species abundance distributions in the three layers reflects the overall community characteristics; (ii) the Poisson lognormal can describe the species abundance distribution in diverse communities with a few abundant species but many rare species; and (iii) 1/α should be an alternative measure of diversity.