Search Results (11)

This study empirically evaluates the energy and carbon reduction effects of an envelope retrofit applied to an aging public building in South Korea. Unlike previous studies that primarily relied on simulation-based analyses, this work fills the empirical research gap by using actual utility billing data collected over one pre-retrofit year (2019) and two post-retrofit years (2023–2024). The retrofit included improvements to exterior walls, roofs, and windows, aiming to enhance thermal insulation and airtightness. The analysis revealed that monthly electricity consumption was reduced by 14.7% in 2023 and 8.0% in 2024 compared to that in the baseline year, with corresponding decreases in electricity costs and carbon dioxide emissions. Seasonal variations were evident: energy savings were significant in the winter due to reduced heating demand, while cooling energy use slightly increased in the summer, likely due to diminished solar heat gains resulting from improved insulation. By addressing both heating and cooling impacts, this study offers practical insights into the trade-offs of envelope retrofitting. The findings contribute to the body of knowledge by demonstrating the real-world performance of retrofit technologies and providing data-driven evidence that can inform policies and strategies for improving energy efficiency in public buildings. Full article

The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R² of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure. Full article

This paper presents a detailed study of the energy savings that can be achieved through residential appliances by implementing conservation voltage reduction (CVR). The computed energy savings are derived from the ZIP coefficients of a load model (static) under varying voltage conditions that represent the power consumed by each appliance tested. Multiple studies have shown energy savings at the substation level but not at the level of a specific residence and appliance; hence, the latter is the focus of this analysis. The appliances are determined based on the type of heating and cooling loads, and the daily duration of use for each appliance, which contributes to the total monthly consumption that is billed for a household are included in this study. Energy savings in Malaysia has been a focus of many current studies, and this study is the first attempt to achieve energy savings through the implementation of CVR. No published work has compared individual appliances that truly benefits from CVR, and this is addressed in this study. The main contribution of this study is that it provides very detailed and measured data that are used to analyze and generate energy savings for very specific residential appliances intended to meet every 1% voltage drop. The load model is developed for the common household appliances in Malaysia, and then a voltage reduction study is applied to the appliances as well as the Malaysian distribution networks. Here, we also provide insight for performing voltage reduction at an electric vehicle (EV) charging station in Malaysia. The results of this study should serve as a foundation for all practicing utilities engineers to address the issue of CVR in Malaysia. The CVR factors for residential, commercial, and industrial networks in Malaysia are also published in this paper. Full article

This study aims to design and optimize a backup renewable energy station and possibility of the grid-connected hybrid photovoltaic (PV) power system for firms in 2nd Jeddah industrial city workshops. Wind and solar energy potentials were examined, and data from a variety of sources were obtained as part of the study process. It is important to utilize the application hybrid optimization model for electric renewables (HOMER) to evaluate relevant data as well as the suggested hybrid power system’s economic feasibility. The system’s payback is solely based on monthly grid bill savings and increased profits due to the absence of a power shortage. The most cost-effective system design is measured in terms of the original cost, ongoing cost, cost per unit, and total system net present value. As a result, fulfilling the load demand with 220 kW wind turbines and 500 kW solar PV is both cost-effective and efficient. The simulation results for the second scenario with a wind turbine show that a combination of a 500 kW PV, 300 kWh battery capacity, 22 kW wind turbine, and 315 kW converter is the most feasible solution for this case study, with SAR 4,433,658 net present cost (NPC) and SAR 0.1741 LCOE. Full article

End-use consumption provides more detailed information than total consumption and reveals the mechanism of energy flow through a given building. Specifically, for weather-sensitive energy end-uses, it enables the prioritization and selection of heating and cooling areas requiring investigation and actions. One of the major barriers to acquiring such heating and cooling information for small- and medium-sized buildings or low-income households is the high cost related to submetering and maintenance. The end-use data, especially for heating and cooling end-uses, of such-sized buildings are a national blind spot. In this study, to alleviate this measurement cost problem, two weather-sensitive energy disaggregation methods were examined: the simplified weather-related energy disaggregation (SED) and change-point regression (CPR) methods. The first is a nonparametric approach based on heuristics, whereas the second is a parametric approach. A comparative analysis (one-way ANOVA, correlation analysis, and individual comparison) was performed to explore the disaggregation results regarding heating and cooling energy perspectives using a measurement dataset (MEA) from eleven office buildings. The ANOVA results revealed that there was no significant difference between the three groups (SED, CPR, and MEA); rather strong correlation was observed (r > 0.95). Furthermore, an analysis of the building-level comparison showed that the more distinct the seasonal usage in the monthly consumption pattern, the lower the estimation error. Thus, the two approaches appropriately estimated the amount of heating and cooling used compared with the measurement dataset and demonstrated the possibility of mutual complements. Full article

The effects associated with the pandemic of COVID-19 have left several consequences. The large confinements have caused formal and independent job losses. Therefore, families have been forced to prioritize their monthly utility bills. Most countries have decided on suspending cuts in utility services and give them payment arrangements for the future instead. In this article, the actions taken in Latin America and Europe are presented. Additionally, for the Chilean case, a mechanism for financing the accumulated debt is proposed. The main objective of this is to establish a procedure that allows users to regain the habit of paying for public utilities (water, electricity and piped gas). Full article

The high incidence of electricity theft, meter tampering, meter bypassing, reading errors, and defective and aged meters, among others, increases utility losses, especially non-technical losses (NTL). A utility in Ghana piloted a non-technical loss reduction program in 2019 to replace postpaid meters with anti-tamper, anti-fraud, and anti-theft smart prepaid meters. By using customer-level residential billing panel data from 2018 to 2019 obtained from the utility, we assess the effectiveness of this program using the difference-in-differences fixed-effect approach. On average, the results indicated that the reported amount of customers’ monthly electricity consumption increases by 13.2% when any tampered postpaid meter is replaced with a smart prepaid meter, indicating the NTLs by customers. We further employed quantile difference-in-differences regression and observed that reported energy consumption has increased for all households except those at the lower quantile (25th quantile). We conclude that smart prepaid metering could be a remedy to reduce NTLs for the electricity distribution sector in areas where electricity theft is rampant. Full article

Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers’ consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson’s chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms. Full article

Recent advances in mobile technologies, especially in the utility payment space, are having an increasingly profound impact on our daily lives and offer advantageous services in the utility sectors. This paper examines the prevalence and patterns of customer uptake of an electronic water payment (EWP) system and its implications for water delivery in the Tema Metropolitan Area, Ghana. Data for the study comprised a survey of 250 utility customers and the review of a 12-month water use and customer payment database from a water supply company. Results indicate that although customers were aware of EWP’s existence, overall uptake was very low. EWP awareness and intention to use EWP were not significantly associated with customers’ gender, phone/mobile money ownership, educational status, and water usage. However, age, employment status, income, and means of receiving monthly bills were found to be statistically different in relation to the awareness of EWP. These findings offer several implications for water service and utility providers to market mobile payment solutions and to increase the consumer uptake of these services and payment options. Full article

This study examines the potential of delayed monthly bill payments as a predictor of suicidal behavior in South Korea with the highest suicide rate among developed countries. Using the Korea Welfare Panel Study, multivariable logistic regressions examined the association between suicide ideation/attempts and the frequency of late payments on utility bills or National Health Insurance premium during last three years. Confounding factors such as past depression and suicide ideation/attempts history were adjusted for. Among 10,988 individuals, 2.7% reported suicide ideation and 0.11% attempted suicide in the past year, while 7.1% reported they paid late once or more during the last three years. Adults with two or more delayed payment had 2.32 times increased odds of suicidal ideation and 10.99 times increased odds of suicide attempts, compared to having no late payments. Adjusting for other socio-economic characteristics rarely changed the relationship between late payments and suicide ideation/attempts (for suicidal ideation, OR = 2.11; for suicide attempts: OR = 7.44), suggesting the independent effect of late payments on suicide behavior. With these findings, it can be suggested that late payment is an important factor, preemptively signaling suicide behavior with serious consequences in health and life. Full article

This paper focuses on the reliability of electricity supply at three different locations in Indonesia, namely in Sumatra, Timor, and Papua, through a comparison of reported indices of power reliability (SAIFI and SAIDI) and experimental results from user surveys and power measurements. The reason for this study is the lack of information about the actual, quantified reliability of power supplied by the main grid in Indonesia, while narratives of end-users indicate the reliability might be unsatisfactory. The study was executed using data from 114 randomly-selected respondents in the city of Pekanbaru in Sumatra, 65 in the city of Kupang in Timor and 26 in the city of Jayapura in Papua, totaling 205 respondents. These users experienced a higher unavailability of power delivered by the grid than expressed by the utility-reported SAIDI and SAIFI. Therefore, for this study, new indices are introduced, namely the Perceived (P) SAIDI and SAIFI, which are based on the frequency and duration of blackouts experienced by the users. It is concluded that the reported reliability indices do not always demonstrate the experience of the grid users. P-SAIFIs were 1.3 to 4.6 times higher in Pekanbaru and Kupang, respectively than the utility-reported SAIFIs for the same provinces. Also, P-SAIDIs were 2.6 to 3.9 times higher in Pekanbaru and Kupang, respectively, than the utilities’ SAIDIs. It is therefore not surprising that depending on the location, 14% to 65% of the users own a backup generator and that households are willing to pay $3 to $8 extra per monthly electricity bill or $1c–$3c per kWh for improved reliability. Full article