Electric Meter Research Articles

An Efficient Cooperative Routing with ML based Energy Efficiency Model for Distributed Underwater WSN Electricity Meter Warning System

Underwater wireless sensor network that operates underwater, typically in oceans, lakes, and rivers. UWSNs are composed of a large number of small sensor nodes that are equipped with various sensing and communication capabilities. These nodes are deployed in the underwater environment to collect and transmit data, which can be used for a variety of applications such as environmental monitoring, oceanography, and marine biology. The Underwater WSN (UWSN) consists of sensor nodes to sense the data and transmit it to the sink node. These sensor nodes (SN) are equipped with limited batteries, which is the central issue. Therefore, the routing protocols were developed for researchers to save energy. However, the increment of network lifetime remains an open challenge. Forwarding the data to the nearest SN to the sink will reduce the network reliability and stability, draining SN's energy early. To overcome these issues, this paper focused on developing an efficient Cooperative based routing (CR) with a machine learning (ML) model to improve the network's lifetime. The cooperative routing discovers the route path from the sender to the destination. The best possible way from the sender to the receiver has been selected using the ML approach called the Self-organizing network (SON). By identifying congestion-free multi-hop transmission using CRSON, the data packet is transmitted from sender to receiver with reduced energy, increasing the network's lifetime and reliability. This model is simulated and experimented with energy efficiency, packet delivery, loss rate, latency, and throughput metrics.

The state primary standard of units of time, frequency and the national time scale GET 1-2022: contribution to the formation of Coordinated Universal Time

Measurement of time and frequency is one of the most widespread types of measurements, information on the exact value of time, on the national time scale is extremely in demand by a wide variety of consumers, ranging from commercial electricity metering systems, where the required synchronization accuracy is a few seconds, to space navigation systems that impose requirements on synchronization at the level of units of nanoseconds. At the same time, consumer requirements for the accuracy of time and frequency measurements, as well as for the efficiency of obtaining time-frequency information, are steadily growing, which entails the need to modernize the means of reproducing, storing and transmitting units of time, frequency and time scale. To meet modern consumer requirements for the accuracy of time and frequency measurements, technical means of reproduction, storage and transmission of units have been introduced into the State primary standard of units of time, frequency and national time scale GET 1-2022, allowing to significantly increase the contribution of GET 1-2022 to the formation of the coordinated universal timescale UTC. A brief overview of the composition of GET 1-2022 is given, a comparative analysis of the contribution of time standards to the formation of the UTC timescale is carried out, as well as an analysis of the shifts of the time scales of the standards relative to UTC and the instability of the frequency standards. It is shown that from September 2022 to March 2023, the contribution of GET 1-2022 to the formation of the UTC increased significantly and exceeded that of the US Naval Observatory standard, and currently the contributions of these standards are comparable. In terms of frequency instability and average contribution to the formation of UTC, the atomic standards of GET 1-2022 are significantly superior to similar standard instruments from other countries. It has been established that the national coordinated time scale UTC(SU) is one of the best national implementations of UTC, and the national atomic time scale TA(SU) occupies a leading position among the time scales of leading foreign time laboratories in terms of instability.

ELECTROMAGNETISM: Application of physics in electrical energy meters

ELECTROMAGNETISM: Application of physics in electrical energy meters

Scheduling of Multi-AGV Systems in Automated Electricity Meter Verification Workshops Based on an Improved Snake Optimization Algorithm

Automated guided vehicles (AGVs) are one of the core technologies for building unmanned autonomous integrated automated electric meter verification workshops in metrology centers. However, complex obstacles on the verification lines, frequent AGV charging, and multi-AGV collaboration make the scheduling problem more complicated. Aiming at the characteristics and constraints of AGV transportation scheduling for metrology verification, a multi-AGV scheduling model was established to minimize the maximum completion time and charging cost, integrating collision-avoidance constraints. An improved snake optimization algorithm was proposed that first assigns and sorts tasks based on AGV-order-address three-level mapping encoding and decoding, then searches optimal paths using an improved A* algorithm solves multi-AGV path conflicts, and finally finds the minimum-charging-cost schedule through large neighborhood search. We conducted simulations using real data, and the calculated results reduced the objective function value by 16.4% compared to the traditional first-in-first-out (FIFO) method. It also reduced the number of charges by 60.3%. In addition, the proposed algorithm is compared with a variety of cutting-edge algorithms and the results show that the objective function value is reduced by 8.7–11.2%, which verifies the superiority of the proposed algorithm and the feasibility of the model.

THE METHOD OF COMPARATIVE EVALUATIONS OF COMMERCIAL OFFERS OF ELECTRICITY SUPPLIERS FOR CONSUMERS WITHOUT HOURLY ELECTRICITY METERING

The article highlights the main approaches to choosing the optimal option for purchasing electricity for consumers without hourly electricity metering, who purchase electricity from suppliers at free prices. The components of the cost of electric energy for the final consumer were investigated and the consumer's ability to influence the value of these components was noted. The method of comparative evaluations for the consumer's choice of the optimal commercial offer from electricity suppliers is presented.

Development of a Machine Learning Framework Based on Occupant-Related Parameters to Predict Residential Electricity Consumption in the Hot and Humid Climate

Occupant-related variables constitute one of the most significant groups of factors influencing residential building energy consumption. However, prediction methods often oversimplify these parameters, leading to substantial discrepancies between predicted and actual consumption. To address this issue, the present study aims to develop a machine learning framework for predicting electricity consumption in residential buildings based on occupant-related factors. The study incorporates twenty-six inputs, including occupant characteristics such as demographics, occupancy, behavior, and behavioral efficiency, two time-related factors, and three extra parameters related to equipment (refrigerator age, hot water source, and type of electricity meter) for training and testing the Random Forest (RF) algorithm in both regression and classification forms. The results indicate that the trained RF regressor exhibits well performance (R2Train = 0.989, R2Test = 0.916, MAETrain = 0.81, MAETest = 2.21, RMSETrain = 1.27, and RMSETest = 3.45). Furthermore, feature importance analysis reveals that the most significant parameter is the time of year, representing weather conditions, followed by the number of occupants, neighborhood, indoor set-point range, mean age of occupants, window opening, and cooling system mode. Even after removing the least impactful factors, the model maintains strong performance with the 16 most important variables (R2Train = 0.986, R2Test = 0.910, MAETrain = 0.83, MAETest = 2.25, RMSETrain = 1.31, and RMSETest = 3.49). Additionally, the RF classifier is designed for problems with 2, 4, 6, and 8 classes based on energy consumption ranges. The results of this model demonstrate that the 2-class model achieves the highest performance (AccuracyTest = 0.963, MAETest = 0.04, and RMSETest = 0.19). However, it lacks detailed categorization of homes based on electricity consumption. On the other hand, the 4-class and 6-class models strike a good balance between prediction performance and the level of detail. In conclusion, the proposed method can accurately predict residential electricity consumption and can serve as a valuable reference for researchers and utility managers when formulating energy reduction policies and comparing the effectiveness of different strategies.

Unlocking Insights: A Cloud Tool for Data Visualisation in a Smart Meter Project

Nowadays, the large amount of data generated by society has led to a dependency on data analysis and visualisation tools. Therefore, the objective of this research was to implement a cloud-based tool to improve the visualisation of data obtained from 4G network simulation on smart meters. Two stages were carried out in order to analyse and process the data using a cloud-based tool to support data visualisation and to understand and facilitate effective decision-making. This resulted in a remarkable 27.39% increase in average data quality, thanks to the authenticity and reliability of the data obtained through the 4G LTE network on smart meters. It also had a significant impact on the percentage of data read and displayed, with an increase of 63.70%. Finally, the percentage of useful data when applying the tool in the cloud also increased by 47.30%. This allows end users to visualise and understand the behaviour of electricity meters with an LTE network through a customised dashboard.

An Attention-Based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Digital water meter digit recognition from images of water meter readings is a challenging research problem. One key reason is that this might be a lack of publicly available datasets to develop such methods. Another reason is the digits suffer from poor quality. In this work, we develop a dataset, called MR-AMR-v1, which comprises 10 different digits (0 to 9) that are commonly found in electrical and electronic water meter readings. Additionally, we generate a synthetic benchmarking dataset to make the proposed model robust. We propose a weighted probability averaging ensemble-based water meter digit recognition method applied to snapshots of the Fourier transformed convolution block attention module (FCBAM) aided combined ResNet50-InceptionV3 architecture. This benchmarking method achieves an accuracy of 88% on test set images (benchmarking data). Our model also achieves a high accuracy of 97.73% on the MNIST dataset. We benchmark the result on this dataset using the proposed method after performing an exhaustive set of experiments.

Online verification and management scheme of gateway meter flow in the power system by machine learning

Currently, the calibration of electric energy meters often involves manual meter reading, dismantling inspection, or regular sampling inspection conducted by professionals. To improve work efficiency and verification accuracy, this research integrates machine learning into the scheme of online verification and management of gateway meter flow in the power system. The approach begins by applying the Faster Region Convolutional Neural Network (Faster-RCNN) model and the Single Shot MultiBox Detector (SSD) model to the recognition system for dial readings. Then, the collected measurement data is pre-processed, excluding data collected under light load conditions. Next, an estimation error model and a solution equation for the electricity meter are established based on the pre-processed data. The operation error of the electricity meter is estimated, and the estimation accuracy is verified using the limited memory recursive least squares algorithm (LMRLSA). Furthermore, business assistant decision-making is carried out by combining the remote verification results with the estimation outcomes. The proposed dial reading recognition system is tested using 528 images of meter readings, achieving an accuracy of 98.49%. In addition, the influence of various parameters on the error results of the electricity meter is also explored. The results demonstrate that a memory length ranging from 600 to 1,200 and a line loss error of less than 5% yield the most suitable accuracy for estimating the electricity meter error. Meanwhile, it is advisable to remove measurement data collected under light load to avoid unnecessary checks. The experiments manifest that the proposed algorithm can properly eliminate the influence of old measurement data on the error parameter estimation, thereby enhancing the accuracy of the estimation. The adjustment of the memory length ensures real-time performance in estimating meter errors and enables online monitoring. This research has certain reference significance for achieving the online verification and management of gateway meter flow in the power system.

Transformative disruptiveness or transition? Revealing digitalization and deep decarbonization pathways in the Italian smart electricity meter roll-out

Energy infrastructure digitalization is proposed as key for a just deep decarbonization. However, an integrative literature review reveals the lack of a more profound exploration on normative considerations tied to world perspectives and values, as levers of a lasting transformation in the context of socio-technical transitions. Thus, the paper employs qualitative research methods to investigate how energy infrastructure digitalization contributes to transformative disruption, along which deliberated deep decarbonization pathways, using Italy's smart electricity meter roll-out as a case study. While the roll-out represents a substantial effort in introducing digital devices into the residential sector, concerns persist about its effectiveness, also due to potential societal disruptions. Through in-depth interviews with experts (N = 17) and citizens (N = 23) and content-oriented analysis, the research unveils evolving pathways between ever more disengaged two grand narratives and value systems: energy as a commodity and energy as a common good. The first opts for technological progress, changing consumer behavior, to address energy poverty and social justice, building on market-based instruments and new business models. Conversely, the second harbors skepticism towards technological fixes, emphasizes energy sufficiency and questions energy opulence, linked to environmental justice considerations, and calling for corrective principles by regulatory means. Finally, the paper presents a conceptual framework to re-shape deep deliberative transformation, highlighting the importance of re-connecting to defined values, re-generating actionable knowledge, restructuring institutions in societal experimentation, and recognizing underlying processes such as justice claims. It argues for further research into discourse coalitions capitalizing on defined narratives, to better understand their impact on policy-making processes.

Predicting energy consumption of chiller plant using WOA-BiLSTM hybrid prediction model: A case study for a hospital building

It is important to study building energy consumption considering the current state of global climate and its close relationship with building energy consumption. This study proposes a hybrid Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-BiLSTM) model to predict energy consumption using a hospital building located in Beijing as a case study. First, a simulation is performed using building simulation software and the model is calibrated using energy bill and electricity meter data. Subsequently, the energy consumption of the calibrated model is decomposed to determine the energy consumption of the chillers and pumps. Finally, a BiLSTM model is established and the WOA is employed to enhance the generalization and performance of the BiLSTM model. Results show 1) for hour scale, the Mean Absolute Percentage Error (MAPE) of the WOA-BiLSTM model is reduced by 2.93 % and 0.75 % over the WOA-LSTM and BiLSTM models, respectively. 2) the time-lag effect of energy consumption is discussed and the results show that the optimal window size is approximately 18 h. 3) the optimization results of different iterations and population size of WOA show that performance is optimal when 400 iterations and a population size of 5, and larger datasets require more iterations based on a comparison of two datasets (hour and day scales).

Development and Implementation of Energy Consumer Metering System with the Integration of Smart Users’ Management Model

An electrical meter with the integration of the Internet of Things (IoT) device measures and records the consumption of electrical energy in residential and commercial buildings. This allows for remote monitoring and management of the energy usage through an IoT platform. This IoT meters emerged due to the inability of the existing metering system to smartly integrate the IoT application into the metering market. With the emergence of IoT technology, microcontrollers and sensors are deployed for this model to avert the aforementioned challenges. The microcontroller is responsible for collecting and processing data from the meter, as well as communicating with the other components of the device. The communication module enables the meter to connect to the Internet and transmit data to the cloud, to be accessed and analyzed through an IoT platform. The voltage and current sensors give input current and voltage signal for the microcontroller for analogue to digital conversion (ADC) effect as programmed. The experimental results indicate that the meter is accurate in measuring energy consumption with a maximum relative error of -6.06% as observed in Case 3 with a rated power of 40W and absolute current of 0.02A. The negative value of the relative error suggest that the measured energy is slightly lower than the actual energy consumed. The developed meter prototype engendered smart energy measurement with modern IoT technologies, this enables remote monitoring and of energy usage.

Assessment of Biomedical Waste Generation in Dialysis Units: A Prospective Observational Study-Is it Time for "Green Dialysis"?

Introduction: Chronic kidney disease and as a consequence end-stage kidney disease (EKSD) is increasing globally. More and more people across the world are requiring hemodialysis (HD). The HD procedure produces a large quantity of biomedical waste. In addition, HD consumes a large quantity of water. In this study, we estimated the waste generated from our government-funded HD unit. Materials and methods: It is a prospective study that was carried out in the dialysis unit in the nephrology department over a period of 1 year. The daily dialysis waste generated by the unit was measured using a spring balance. The proportion of plastic and nonplastic waste was determined. The quantity of biomedical waste generated per person in 1 year was calculated. Water input to the dialysis unit was noted. Water consumption per dialysis was calculated. Liquid chemical waste consumed was determined. Electricity consumed by the unit was measured by the electricity meter. The cost of waste disposal was calculated. The cost of electricity consumption and water consumption was also calculated. Results: The approximate weight of waste disposables generated in one dialysis was 0.75 kg. Approximately each person generates 1.29 kg of waste per dialysis. Each dialysis required 125 L of reverse osmosis (RO) water and to generate 125 L of RO water 250 L of raw water was used. This happens as 125 L of water are rejected during the generation of 125 L of RO water. Thus, the net water consumption for each dialysis was 250 L. Chemical waste generated per dialysis includes 90 mL citric acid per dialysis and 130 mL bleach. Each dialysis consumes 3 kWh (three units) of electricity. The cost of electricity for each dialysis was 25.5 INR and the cost of water was 25 INR per dialysis. The cost of waste disposal for each dialysis bed was 6 INR. Discussion: Each dialysis patient produced 1.29 kg of waste per dialysis which was like other studies. Unlike other studies, the waste was not being reprocessed or recycled. Conclusion: Hemodialysis produces substantial biomedical waste. Proper waste disposal techniques and policies to promote reduction, reuse, and recycling will go a long way toward promoting green dialysis and reducing environmental as well as economic burdens. How to cite this article: Sahay M, Sahay RK, Seshadri B, et al. Assessment of Biomedical Waste Generation in Dialysis Units: A Prospective Observational Study-Is it Time for "Green Dialysis"? J Assoc Physicians India 2023;71(10):49-52.

Mapping urban living standards and economic activity in developing countries with energy data.

Urban data deficits in developing countries impede evidence-based planning and policy. Could energy data be used to overcome this challenge by serving as a local proxy for living standards or economic activity in large urban areas? To answer this question, we examine the potential of georeferenced residential electricity meter data and night-time lights (NTL) data in the megacity of Karachi, Pakistan. First, we use nationally representative survey data to establish a strong association between electricity consumption and household living standards. Second, we compare gridded radiance values from NTL data with a unique dataset containing georeferenced median monthly electricity consumption values for over 2 million individual households in the city. Finally, we develop a model to explain intra-urban variation in radiance values using proxy measures of economic activity from Open Street Map. Overall, we find that NTL data are a poor proxy for living standards but do capture spatial variation in population density and economic activity. By contrast, electricity data are an excellent proxy for living standards and could be used more widely to inform policy and support poverty research in cities in low- and middle-income countries.

A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

The increasing population migration to urban and peri-urban areas increases basic service needs for cities worldwide. Residential electricity demand increases with more customers and varies with novel uses, such as charging electric vehicles, which may add additional dynamics to the residential electricity demand profile. Widespread installation of smart electricity meters provides fine temporal resolution data reflecting current demands and supports predicting future demands. As part of a demand-side management program, understanding the main drivers of current electricity usage based on demand-driven and exogenous predictors represents a valuable tool for utilities facing new demand scenarios. This work presents the application of multiple models to forecast electricity demand based on the input data and the forecasting horizon. Models with exogenous variables as predictors are part of the input–output category, including a Feed Forward Neural Network, Random Forest, and a Linear Gaussian State Space model. The second category is demand-driven models, where predictors include only previous demand values. The demand-driven models in our analysis include a univariate Nonlinear Autoregressive Neural Network and a Linear Gaussian State Space. Using smart electricity meter data from the greater Chicago area, we compare the performance of the models on two different types of accounts: single- and multi-family residential users when forecasting one and multiple steps. Results show that the Linear Gaussian model reports an R2 of 0.99 compared to an average R2 of 0.92 from the Feed Forward Neural Networks and Random Forest when forecasting single- and multi-family electricity demand one step ahead. However, Nonlinear Autoregressive Neural Networks report an average R2 of 0.85 compared to the Linear Gaussian R2 of 0.58 when forecasting 48 steps. We also found that the most important predictors for single-family demand are temporal variables like weekdays, working and non-working days, and day hours. For multi-family demand, electricity demand at the same hour as the previous week replaces weekdays as a significant predictor. Different forecasting models can assist utilities and city planners to manage demand under different and novel residential electricity usage conditions.

Money or Power? Choosing Covid-19 aid in Kenya

In response to the Covid-19 crisis, 186 countries implemented direct cash transfers to households, and 181 introduced in-kind programs that lowered the cost of utilities such as electricity, water, transport, and mobile money. During times of crisis, do people prefer in-kind transfers or cash, and why? In this paper, we compare electricity transfers against a benchmark of cash transfers (mobile money) among 2000 rural and urban residents of Kenya with pre-paid electricity meter connections. We offer participants an incentivized choice between electricity transfers or mobile money, totaling approximately USD 10 to 15, and then implement their choice over three months. We generate three main findings. First, participants overwhelmingly prefer cash, with three-quarters of participants opting for mobile money even when offered electricity tokens with a cash value that is 40 percent higher, possibly due to the flexibility in expenditures or credit constraints. Second, despite relatively low baseline electricity consumption, preference for cash is slightly lower in rural areas, possibly due to higher transaction costs for purchasing electricity, lower mobile money penetration, or savings constraints. Third, electricity tokens transfers generate a larger increase in electricity consumption than equivalent cash transfers, suggesting a role for mental accounting; however, we estimate no impact of either electricity or cash transfers on a broad set of socioeconomic outcomes. These patterns suggest that mobile money transfers generate larger welfare gains than electricity credit, at least in settings with high mobile money penetration.

THE COVID-19 PANDEMIC OUTCOMES ON FOOD WASTE AND CARBON FOOTPRINT OF CAFÉS IN BANDAR BARU BANGI AND SUNGAI PETANI, MALAYSIA

Cafés, a subcategory of the foodservice industry, are popular as a place to eat and meet with others, among Malaysians and the world population. Their operation partly contributes to food waste and carbon footprint. However, the volume of these parameters fluctuated throughout the COVID-19 pandemic. Further, there is a need to diversify the food waste data of foodservice establishments in Malaysia, across different regions and cultures. Therefore, we conducted an environmental assessment of ten cafés in two of Malaysia’s cafe hot spots, Sungai Petani and Bandar Baru Bangi, to determine their food waste (preparation loss and plate loss) and carbon footprint generation in a week when the COVID-19 restrictions were relaxed. Data on carbon footprint were collected by reading the electricity and water meters and calculated using emission factors. Sungai Petani generated substantially more food waste (77.68 % in total, equivalent to 93.04 kg) than Bandar Baru Bangi (22.32 %, equivalent to 26.73 kg), as revealed by material flow and descriptive analyses. Similarly, the carbon footprint from water consumption in Sungai Petani was 1.03 kgCO2e, generated from 3m3 water consumption, whereas Bandar Baru Bangi produced 0.82 kgCO2e of carbon footprint from 2.39 m3 water consumed, both districts on a Sunday. Contrarily, the carbon footprint related to the electricity usage in both locations was comparable. The largest amount of carbon footprint produced in Sungai Petani was on Sunday, while Saturday showed the highest carbon footprint in Bandar Baru Bangi. These findings will help the future redirection of Malaysia cafés’ management towards more sustainable practices, which can be enhanced with the help of information on customer footfall.

Enhanced linearity through high-order antisymmetric vibration for MEMS DC power sensor

We present an electric power meter that capitalizes on the interaction of electrothermal strain and mechanical vibration in a micro-electro-mechanical systems (MEMS) beam undergoing the antisymmetric mode of vibration. This is achieved by using a resonant bridge driven with an electrothermal modulation technique. The change in electrical power is monitored through the alteration in the mechanical stiffness of the structure, which is tracked electrostatically. The observed deflection profile of the beam under the influence of electrothermal effects shows that the deflection geometry due to buckling exhibits similar trends as the first symmetric vibrational mode, in contrast to the antisymmetric mode. Therefore, we compare two distinct vibrational modes, converting the compressive thermal stress generated by the input electrical power via Joule heating into a shift in the resonance frequency. By employing antisymmetric vibrational mode, the output of our device is consistently monotonic to the input electrical power, even when the microbeam is experiencing buckling deflections. In addition, the sensing operation based on antisymmetric modes yields only a 1.5% nonlinear error in the response curve, which is ten times lower than that of symmetric modes. The observed deformation shape of the resonator agrees with the results obtained from multi-physics finite simulations. Finally, this approach has the potential to be extended to other frequency-shift-based sensors, allowing for higher linearity.

Benford’s Law in Electric Distribution Network

Benford’s law can be used as a method to detect non-natural changes in data sets with certain properties; in our case, the dataset was collected from electricity metering devices. In this paper, we present a theoretical background behind this law. We applied Benford’s law first digit probability distribution test for electricity metering data sets acquired from smart electricity meters, i.e., the natural data of electricity consumption acquired during a specific time interval. We present the results of Benford’s law distribution for an original measured dataset with no artificial intervention and a set of results for different kinds of affected datasets created by simulated artificial intervention. Comparing these two dataset types with each other and with the theoretical probability distribution provided us the proof that with this kind of data, Benford’s law can be applied and that it can extract the dataset’s artificial manipulation markers. As presented in the results part of the article, non-affected datasets mostly have a deviation from BL theoretical probability values below 10%, rarely between 10% and 20%. On the other side, simulated affected datasets show deviations mostly above 20%, often approximately 70%, but rarely lower than 20%, and this only in the case of affecting a small part of the original dataset (10%), which represents only a small magnitude of intervention.

Analisis Sistem Informasi Akuntansi Pendapatan Penerimaan Kas Listrik Prabayar Pada PT Ajamu Faadhilah Agung

This research aims to determine the accounting information system process regarding cash receipts from new prepaid electricity connections and meter replacement from postpaid to prepaid as implemented by PT. AJAMU FADHILAH AGUNG which is located at Pasar VII Street No. 2 Gg. Karya Rotan Sei Rotan Village. PT AJAMU FAADHILAH AGUNG is a company operating in the electrical and mechanical sector as a provider of electrical power installation services in Indonesia, so it is hoped that it can play an active role in supporting government policies together with PT. PLN (Persero). This research is limited to new connections to prepaid electricity and replacing meters from postpaid to prepaid. Problem formulation: 1. What is the accounting information system regarding cash receipts? 2. What are the advantages and disadvantages in implementing information systems. Research objectives: 1. To find out the application of accounting information systems regarding cash receipts. 2. To find out the advantages and disadvantages of applying accounting information systems regarding cash receipts. The data collection method used in this research is decision technique and direct research in the field. The technical analysis used is the description method, namely a method carried out by analyzing the cash receipts related to new prepaid electricity connections and meter replacement from postpaid - prepaid.