Search Results (8)

Electricity theft remains a persistent challenge, particularly in developing economies where infrastructure limitations and socioeconomic disparities contribute to illegal connections. This study analyzes the determinants influencing electricity theft in Kinshasa, the Democratic Republic of Congo, using a logistic regression model applied to 385 observations, which includes random bootstrapping sampling for enhanced stability and power analysis validation to confirm the adequacy of the sample size. The model achieved an AUC of 0.86, demonstrating strong discriminatory power, while the Hosmer–Lemeshow test (p = 0.471) confirmed its robust fit. Our findings indicate that electricity supply quality, financial stress, tampering awareness, and billing transparency are key predictors of theft likelihood. Households experiencing unreliable service and economic hardship showed higher theft probability, while those receiving regular invoices and alternative legal energy solutions exhibited lower risk. Lasso regression was implemented to refine predictor selection, ensuring model efficiency. Based on these insights, a multifaceted policy approach—including grid modernization, prepaid billing systems, awareness campaigns, and regulatory enforcement—is recommended to mitigate electricity theft and promote sustainable energy access in urban environments. Full article

The purpose of this research is to investigate the possibilities of electricity consumption-associated cost reduction in buildings owned by a medium-sized logistics company in Latvia (A_LV), which is a part of the larger international business ecosystem (A). The company is not using all of its facilities for its own business needs, some of them are rented out, and therefore the possibility of impacting electricity consumption in rented out buildings is limited. During the research, mixed-type approaches combining qualitative and quantitative research methods and data analysis were employed, where the quantitative methods helped to analyze the company’s electricity consumption and cost changes in different time periods, while the qualitative methods were used in a literature review. As primary data sources, A_LV’s internal electricity consumption reports and invoices for electricity payments were used, along with publicly available data on electricity consumption in Latvia and wholesale market price fluctuations. Although A_LV has numerous areas of electricity consumption optimization, this research is limited to few of them—lighting system optimization, energy management and automation applications, forklift charging regime adjustments, and choice of electricity retailer and tariff plan. Full article

In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices. Full article

Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO₂ emissions or communications load. Full article

Light emitting diode (LED) and compact fluorescent light (CFL) lamps are widely used because they are associated with low energy consumption and a reduced environmental impact. In the present paper, a study of the real consumption of these devices has been carried out. To do this, the active, reactive, distortion and apparent power and electrical efficiency for various lamps have been measured and calculated. The distortions produced in the network provoke the consumed energy to be in the order of 50–75% higher than that which appears in the commercial characteristics. This situation means that for its operation, it is necessary to generate and distribute an amount of energy much higher than that which is declared as the consumption of these lamps, and so far, this amount of energy is neither quantified nor invoiced. Additionally, groups of lamps have also been studied to check whether, when working together on the same network, there is a compensation phenomenon that reduces the negative effects of individual lamps. We have found that this compensation effect does not occur for the type of devices evaluated. Full article

The renewable energy transition has introduced new electricity tariff structures. With the increased penetration of photovoltaic and wind power systems, users are being charged more for their peak demand. Consequently, peak shaving has gained attention in recent years. In this paper, we investigated the potential of peak shaving through battery storage. The analyzed system comprises a battery, a load and the grid but no renewable energy sources. The study is based on 40 load profiles of low-voltage users, located in Belgium, for the period 1 January 2014, 00:00–31 December 2016, 23:45, at 15 min resolution, with peak demand pricing. For each user, we studied the peak load reduction achievable by batteries of varying energy capacities (kWh), ranging from 0.1 to 10 times the mean power (kW). The results show that for 75% of the users, the peak reduction stays below 44% when the battery capacity is 10 times the mean power. Furthermore, for 75% of the users the battery remains idle for at least 80% of the time; consequently, the battery could possibly provide other services as well if the peak occurrence is sufficiently predictable. From an economic perspective, peak shaving looks interesting for capacity invoiced end users in Belgium, under the current battery capex and electricity prices (without Time-of-Use (ToU) dependency). Full article

Despite its environmental benefits, the amount of Electric Vehicles (EVs) in use within the European Union 28 is still very limited. Poor penetration might be explained by certain factors that dissuade potential buyers. To balance these factors and promote electro-mobility, Member States have established incentives to increase demand. However, the various measures are scattered. This paper contributes to fill the gap in the literature by offering an overall view of the main measures. The authors will focus on measures to promote electro-mobility within the EU28 until 2014. After an in-depth and comprehensive review of the relevant measures, the authors conclude that the most important policy instruments to promote EVs are tax and infrastructure measures in addition to financial incentives for purchasing and supporting R&D projects. Regardless of the scarcity of EV registration data, the available information allows us to conclude that higher EV penetration levels appear in countries where the registration tax, the ownership tax, or both taxes have developed a partial green tax by including CO₂ emissions in the calculation of the final invoice. Full article

The aim of this paper is to identify the financial impact imposed by cost containment measures and especially by the feed in tariff (FiT) reduction upon the profitability of different photovoltaic (PV) investments and the electricity charge faced by consumers. A fully parametric analysis is carried out by varying the following parameters: total installation costs based on their activation date, interest rate for a bank loan, use of a construction subsidy, tax imposition levels, the solidarity surcharge differentiated by the activation and the purchase agreement date and the issuance of credit invoice. During the simulations the simple payback period, the internal rate of return and the profitability index were calculated for the most common investment cases. These were identified through empirical observations on the deployment of PV stations. The profitability of PV stations connected to the medium voltage network were by far affected the most by the cutback while farmers’ PV stations and PV rooftop systems were comparatively less affected. Parameters such as the size of the station and the PV activation date were crucial for the assessment of the viability of PV stations. From a social perspective, the FiT cutback prevented an additional 40% increase in the electricity charge paid by electricity consumers. Full article