Efficient Energy Management for a Proposed Integrated Internet of Things-Electric Smart Meter (2IOT-ESM) System

In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53 and the cases of the electrical energy theft if any below 20 and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected. In this work, an efficient energy management (EEM) approach is proposed to merge IoT technology to enhance electric smart meters by working together to satisfy the best result of the electricity customer's consumption. This proposed system is called an integrated Internet of things for electrical smart meter (2IOT-ESM) architecture. The electric smart meter (ESM) is the first and most important technique used to measure the active power, current, and energy consumption for the house’s loads. At the same time, the effectiveness of this work includes equipping ESM with an additional storage capacity that ensures that the measurements are not lost in the event of a failure or sudden outage in WiFi network. Then then these measurements are sent using the internet of thing (IoT) technology to Google Firebase cloud, where the electric consumer's service center is located to store, analyze the measured data, and detect cases of energy penetration when it exceeds 53 and the cases of the electrical energy theft if any below 20 and then take the appropriate decision about it. Finally, an electric smart metering application (ESM-app) is designed and implemented to read and pull data information from the Google firebase cloud and then send the electric bill to the end consumer, and sending alert messages to the thieves and electrical power hackers to prohibit them if something wrong has detected.

AN AUTOMATED ENERGY BILL METERING SYSTEM BASED ON GSM TECHNOLOGY

The measurement of the energy consumed by residential and commercial buildings by utility provider is important in billing, control, and monitoring of the usage of energy. Traditional metering techniques used for the measurement of energy are not convenient and is prone to different forms of irregularities. These irregularities include meter failure, meter tampering, inaccuracies in billing due to human error, energy theft, and loss of revenue due to corruption, etc. This research study proposed the design and construction of a microcontroller-based electric energy metering system using the Global System for Mobile communication (GSM) network. This system provides a solution to the irregularities posed by the traditional metering technique by allowing the utility provider have access to remote monitoring capabilities, full control over consumer load, and remote power disconnection in the case of energy theft. Proteus simulation software was used to model the system hardware and the software was obtained by using embedded C programming and visual basic. It was observed that the system could remotely take accurate energy readings, provided full control over consumer loads and execute remote disconnection in case of energy theft. The system provides high performance and high accuracy in power monitoring and power management.

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

Aggregated Technical Commercial and Collection Loss Mitigation Through a Smart Metering Application Strategy

The privatization exercise that led to the creation of 11 distribution companies (Discos) is yet to yield the result of meeting the electricity supply needs of Nigeria. It is in realization of this fact that the Federal Government of Nigeria recently made different proposals and regulations to increase efficiency, availability, and competitiveness within the power industry. This article establishes a technical gain ratio to measure the impact of scaling up smart metering on the aggregated technical commercial and collection (ATC&C) losses. The ATC&C loss is the difference between the amount of electricity received by a distribution company from the transmission company and the amount of electricity for which it invoices its customers plus the adjusted collections loss. To achieve this objective, reviews of historical data of the Discos ATC&C losses and customers’ metering records as published by the Nigerian Electricity Regulatory Commission (NERC) for 2015, 2016, 2017, 2018, and 2020 were carried out. In addition, efforts were made to mathematically model the relationship between % metering and % ATC&C losses as this was the framework to help carry out effective forecasts and analyses of the study in order to show the impact level of the strategy employed. One of the salient technical contributions of this article was that it established that for every 1% increase in metering installation, there is a proportionate 0.8% decrease in ATC&C losses, provided all other factors responsible for technical and commercial losses remain constant. Consequently, improved ATC&C loss reduction would be achieved if Discos adopt a combination of other strategies that would ensure reduction in technical and commercial losses in addition to aggressive deployment of meter assets. However, in practice, factors causing technical and commercial losses are never constant as system components depreciate/burn out with time, energy theft, and pilferage, and meter tamper/bypass is on the increase daily; meter deployment is not aggressive enough to match utility customers’ growth. Hence, the adoption of combined modern strategies in addition to aggressive metering in tandem with customers’ growth has to be employed by Discos in a deliberate attempt to reduce ATC&C losses.

Evolution of Electricity Metering Technologies in Nigeria

Advancement in technology has continuously driven the evolution of metering devices and infrastructure in the world and has resulted in more accurate and user-friendly devices equipped with customer interaction interfaces. The evolution of metering technology in Nigeria arose with the unbundling of the National Electric Power Authority (NEPA) but have not progressed smoothly and successfully despite the implementation of various reforms and policies in the Nigerian electricity industry. The persisting problems in the electricity distribution system such as energy theft, vandalism, energy wastage, high line losses can be overcome by the deployment of appropriate metering infrastructure. In the second quarter of 2020, the Nigerian Electricity Regulatory Commission revealed that the total registered customers and total metered customers are 10,516,090 and 4,234,759 respectively leaving a metering gap of 59.73%; after 124 years of commercial electricity availability in Nigeria. This paper discusses Nigeria's metering history and the challenges encountered in the transition of policies, technologies and government reforms. The paper also proposes the way forward to a successful transitioning into a smart distribution grid.

Theft Control Based Master Meter Using Different Network Technologies

The power theft is one of the main problems facing the electric energy sector in Iraq, where a large amount of electrical energy is lost due to theft. It is required to design a system capable of detecting and locating energy theft without any human interaction. This paper presents an effective solution with low cost to solve power theft issue in distribution lines. Master meter is designed to measures the power of all meters of the homes connected to it. All the measured values are transmitted to the server via GPRS. The values of power for all energy meters within the grid are also transmitted. The comparison between the power of the master meter and all the other meters are transmitted to the server. If there is a difference between the energy meters, then a theft is happened and the server will send a signal via GSM to the overrun meter to switch off the power supply. Raspberry pi is used as a server and equipped and programmed to detect the power theft.

Energy Theft Detection in an Edge Data Center Using Deep Learning

With the development of smart grid information physical systems, some of the data processing functions gradually approach the edge layer of end-users. To better realize the energy theft detection function at the edge, we proposed an energy theft detection method based on the power consumption information acquisition system of power enterprises. The method involves the following steps. In the centralized data center, K-means is used to decompose a large amount of data into small data and then input and train neural network parameters to realize feature extraction. We design a neural network named DWMCNN, which can extract features from the day, week, and month and can extract more accurate features. In the edge data center, the random forest (RF) algorithm is used to classify the extracted features. The experimental results show that the clustering method accords with the idea of edge computing-distributed processing and improves the operation speed and that the feature extractor has good convergence performance. In addition, compared with the methods based on various classifiers, this method has higher accuracy and lower computational complexity, which is suitable for the deployment of edge data centers.