Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

Measuring the inequality using energy consumption data in China

With the support by the National Natural Science Foundation of China,the research team led by Prof.Wei Chu(魏楚)and Prof.Zheng Xinye(郑新业)at the Department of Energy Economics,School of Economics,Renmin University of China,measures the inequality using the household energy

Survey of Cereal Consumption Habits in the Community of Djougou, Benin

The cereal group occupies a prominent place in the dietary habits of people in northern Benin and there is little recent information on cereal consumption. This study aims to assess the consumption, acquisition and supply of cereals to households in the community of Djougou. A semi-directive survey with KoBoCollect was conducted among 369 households to collect individual cereal food consumption data. The survey data processed by statistical tools showed that the most consumed cereals are maize (95%, p = 0.887), millet (58%, p = 0.755), rice (55%, p = 0.753), sorghum (15%, p = 0.635), wheat (5%, p = 0.920) and fonio barely 5%. The most common mode of acquisition in Djougou is purchase (50%, p = 0.947) but donation is also observed (25%, p = 0.988) as well as production observed in 20.6% of households. Purchases are made from retailers in local markets (45%, p = 0.920) but also in streets and alleys (30%, p = 0.765). The most widely used preservation technique is drying at room temperature (70%, p = 0.995). Households most often dry in the areas provided in the field (50%, p = 0.783) and at home (40%, p = 0.643). The preferred storage location is the kitchen (60%, p = 0.790). The bedroom (20%, 0.669) and the store (15%, 0.522) are the alternative places for storing cereals. In addition, the supply costs of cereals increased between 2020 and 2021. This vertiginous rise in prices is due, among other things, to the covid19 pandemic. The various data generated not only make it possible to have fresh data but also to invest them in the assessment of health risks for the achievement of a high level of protection of the health and life of consumers.

Security Threat and Data Consumption as Mojor Nuisance of Social Media on Wi-Fi Network

This research is about the nuisances of social media applications on a Wi-Fi network at a university campus in Ghana. The aim was to access the security risk on the network, the speed of the network, and the data consumption of those platforms on the network. Network Mapper (Nmap Zenmap) Graphical User Interface 7.80 application was used to scan the various social media platforms to identify the protocols, ports, services, etc. to enable in accessing the vulnerability of the network. Data consumption of users’ mobile devices was collected and analyzed. Device Accounting (DA) based on the various social media applications was used. The results of the analysis revealed that the network is prone to attacks due to the nature of the protocols, ports, and services on social media applications. The numerous users with average monthly data consumption per user of 4 gigabytes, 300 megabytes on social media alone are a clear indication of high traffic as well as the cost of maintaining the network. A URL filtering of the social media websites was proposed on Rockus Outdoor AP to help curb the nuisance.