Academic literature on the topic 'Electricity Customer Grouping'

<span>Automatic meter reading (AMR) is a reading system result the measurement of electrical energy consumen, both locally and remotely. The problems faced is the high non-technical shrinkage of AMR customers due to installation, maintenance errors as well as dishonest actions some consumers, this has a major influence on electrical power losses. PT. PLN Disjaya currently faces difficulties having to choose which customers should be checked first, so the field can only find a little damage. The K-means method based on historical electric power usage and determine the most optimal number of groups the davies-bouldin index (DBI) method. Based on the results of testing with 2-6 sets of clusters, the cluster set results are the most optimal is set cluster 4 because it has the smallest DBI value 0.893. The set of 4 clusters has the best performance in data grouping of historical power usage of AMR customers the business class, each centroid of each cluster is used as an attribute and value of the AMR customer power usage business chart. The testing phase is customers who categorized as customers with un-normal usage electricity power. The test is, by determining the distance data testing each centroid in the cluster 4 set.</span>

Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using sliding windows. Despite the proposal of many clustering techniques applicable for grouping the observations of a single data stream, only a few of them are focused on splitting the whole data streams into the clusters. In this article we aim to explore individual characteristics of electricity usage and recommend the most suitable tariff to the customer so they can benefit from lower prices. This work investigates various algorithms (and their improvements) what allows us to formulate the clusters, in real time, based on smart meter data.

Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer’s measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities’ databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project.

Currently, thanks to the rapid development of wireless sensor networks and network traffic monitoring, the data stream is gradually becoming one of the most popular data generating processes. The data stream is different from traditional static data. Cluster analysis is an important technology for data mining, which is why many researchers pay attention to grouping streaming data. In the literature, there are many data stream clustering techniques, unfortunately, very few of them try to solve the problem of clustering data streams coming from multiple sources. In this article, we present an algorithm with a tree structure for grouping data streams (in the form of a time series) that have similar properties and behaviors. We have evaluated our algorithm over real multivariate data streams generated by smart meter sensors—the Irish Commission for Energy Regulation data set. There were several measures used to analyze the various characteristics of a tree-like clustering structure (computer science perspective) and also measures that are important from a business standpoint. The proposed method was able to cluster the flows of data and has identified the customers with similar behavior during the analyzed period.

Groupon is the fastest growing company in the daily deal social e-commerce arena. For this reason, their growth and globalization strategy is of particular interest to any researcher or investor interested in understanding this industry and its potential future growth and development. In this first follow-up article on mergers and acquisitions as Groupons primary growth and globalization strategy, the researchers discuss the structural and technological implications of expanding into developing international markets. The research method utilized in this article was a case study. In a previous article, the researchers evaluated Groupons success in utilizing M&As as its growth and globalization strategy, by applying Ruess and Voelpels (2012) Post Merger Integration (PMI) Scorecard. The factors discussed under the PMI Scorecard included strategic, structural, personnel, cultural and stakeholder integration. In this article, the researchers take an in-depth look at structural and technological implications of acquisitions made by Groupon in developing international markets.A well-developed transport and communications infrastructure network is a pre-requisite for the access of less-developed communities to core economic activities and services (Schwab, Sala-i-Martin & Greenhill, 2011). Effective economies therefore depend on sufficient and uninterrupted electricity supply that would enable unimpeded business functioning. The ability to adopt existing technologies, specifically information and communication technologies (ICTs) is indicative of a countrys level of technological readiness. In countries with limited access to the Internet, mobile broadband, smartphones and social media (like Facebook), Groupon has not managed to build an extensive database of customers. Although they may have reached a particular level of customer saturation in the USA, it cannot be argued that they have succeeded in building the same extensive network of customers in other markets, which calls into question the spending on acquisitions in other markets in order to expand their global reach.

Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: (1) using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; (2) the optimal number of clusters for representing residential electricity use profiles is determined; and (3) an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator’s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.

Classification of K Nearest Neighbors in this study to determine the grouping in seeing the suitability of the installed household electricity customers. Then the system built can see customers who want to know the amount of power given and want to add new. Conversely, if customers who want to reduce the power that has been given because it is too large with the condition of houses that are not large and not much use, can be seen in this system. The purpose of this study is to facilitate old customer customers in seeing the installed power with a variable amount of air conditioner (AC), number of refrigerators, number of washing machines and other electronic quantities based on the grouping of test data. first adjusted to the new test data. The process of the K-Nearest Neighbor method is to input the customer's name with the value of the amount of air conditioner (AC) with a value of 2, the number of refrigerators with a value of 2, the number of washing machines with a value of 1 and the number of other electronics with a value of 7. Then the data is seen with distance closest is 1.73205 by being trained by seeing neighbors nearby in training training. Furthermore, training of the data was obtained by customers with ID P-05 found in class C2 classifications. The results of this system are in the form of customer grouping which is categorized into 4 ampere, 6 ampere or 12 ampere category classification types, each of which is seen from the amount of power installed. This research is expected to help PLN customers of the city of Lhokseumawe in knowing the old customers who are included in the type of grouping.Keywords: Classification, Electrical Power, K-Nearest Neighbors