Electricity Usage Research Articles

The design and implementation of Intelligent-Electricity Consumption and Billing Information System (IEBCIS)

The increasing demand for electricity and the need to reduce carbon emissions have made optimizing energy usage and promoting sustainability critical in the modern economy. This research paper explores the design and implementation of an Intelligent-Electricity Consumption and Billing Information System (IEBCIS), focusing on its role in addressing electricity sustainability challenges. Using the Design Science Research (DSR) methodology, the system’s architecture collects, analyses, and visualizes electricity usage data, providing users with valuable insights into their consumption patterns. The research involved developing and validating the IEBCIS prototype, with results demonstrating enhanced real-time monitoring, load shedding schedules, and billing information. These results were validated through user testing and feedback, contributing to the scientific knowledge of intelligent energy management systems. The contributions of this research include the development of a framework for intelligent energy management and the integration of data-driven insights to optimize electricity consumption, reduce costs, and promote sustainable energy use. This research was conducted over a time scope of two years (24 months) and entails design, development, pilot test implementation and validation phases.

Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework

This study aims to develop an efficient model to predict the arrears behavior of electricity users by integrating multi-scale feature learning with a backpropagation (BP) neural network. The goal is to provide accurate early warning systems and enhanced risk management tools for power companies. The BP neural network algorithm adjusts weights to minimize prediction errors, while multi-scale feature learning captures the diversity and regularity of user behavior by extracting data from various time dimensions, such as daily, weekly, and monthly intervals. First, electricity usage and weather data from the UMass Smart Dataset are preprocessed, including steps such as data cleaning, standardization, and normalization. Next, features are extracted across three time scales—daily, weekly, and monthly. These features are then input into the BP neural network model using the multi-scale feature learning method. A hierarchical neural network structure is designed to address the characteristics of different scales in distinct layers. Key model parameters are optimized, and a sensitivity analysis is conducted. The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. Specifically, the Gini coefficient is 0.55, the Kolmogorov-Smirnov statistic is 0.60, the Matthews correlation coefficient is 0.45, and specificity is 0.82. These results indicate that the proposed method offers significant improvements in capturing user behavior patterns and enhancing prediction accuracy. The study concludes that the effective fusion of multi-scale features not only enhances the model’s prediction performance but also strengthens its generalization ability. This method provides an advanced risk management tool for power companies, helping to increase the operational efficiency of smart grids and encouraging further research toward greater intelligence in the field.

Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network

Currently, the time-of-use pricing model for electricity focuses on a single objective, often overlooking various factors that influence electricity costs. This oversight can lead to significant disparities in peak and off-peak electricity usage within the distribution network following optimization. Therefore, a new time of using electricity price optimization method is proposed that takes into account the losses of distributed photovoltaic access to the distribution network. Considering the topology structure of the distribution network after the integration of distributed photovoltaic, this paper calculates the comprehensive losses generated by the operation of the distribution network. Also, this paper constructs a time of use electricity price optimization mathematical model with the objectives of minimizing network loss, minimizing load variance, minimizing peak valley difference of equivalent load, and maximizing user satisfaction. And refer to the basic requirements for electricity pricing in the distribution network, set a series of constraints for optimizing electricity prices. Applying an improved imperialist competition algorithm this paper integrates Tent chaotic reverse learning to solve a multi-objective optimization model and obtain an optimized time of use electricity pricing plan. The experimental results show that after the implementation of this optimization method, the peak valley difference of the daily power load curve of the distribution network is only 350 MW, demonstrating superior peak shaving and valley filling effects.

Personalized recommendation algorithm based on electricity users’ usage behavior portrait

Personalized recommendation algorithm based on electricity users’ usage behavior portrait

Analysis of vehicle-to-grid economy based on sustainable development grounded in game theory

In a vehicle-to-grid (V2G) peaking system based on coordination games, ultimate goal realized a positive total net return in V2G operations, while ensuring that the interests of all stakeholders reached a balanced equilibrium. This study examined the cost and benefit dynamics of BYD (Build Your Dreams) electric vehicles, encompassing electric vehicle users, power grid companies, and power plants, before and after participating in V2G peak load balancing services. Pre-game analysis revealed that power plants led in terms of revenue, significantly surpassing electric vehicle users, whereas grid companies incurred negative net income. An increase in peak load exacerbated the power company’s net loss, posing a significant challenge to V2G’s sustainable development. However, post-game, a cooperative alliance between the power grid company and car users was forged through financial means, with the power grid company and power plants offering invaluable data support through collaborative efforts. The disparity between optimized peak and base load generation costs narrowed to 79% of pre-optimization levels, with a 3.14% reduction in carbon emissions. Furthermore, the implementation of this collaboration strategy can result in a maximum reduction of 9% in the power grid company’s losses and an enhancement of 6.7% in the net revenue of the entire V2G peak-shaving system. Consequently, this strategy significantly promotes the development of V2G operations.

Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage with Conventional Meters: A Case Study of Industrial and Business Tariff Customers from PT PLN (Persero) Indonesia

Classification Based on the Support Vector Machine for Determining Operational Targets for Controlling Electricity Usage with Conventional Meters: A Case Study of Industrial and Business Tariff Customers from PT PLN (Persero) Indonesia

The value of now, later, or never: assessing the value of electricity users' flexibility

The value of now, later, or never: assessing the value of electricity users' flexibility

CARBON REDUCTION STRATEGIES BY THE HOSPITALITY SECTOR: A GLOBAL SOUTH PERSPECTIVE

Climate change is an imminent crisis faced by the global community and tourism is identified as a significant sectoral contributor. The purpose of this study, therefore, is to investigate the carbon reduction strategies of the hospitality sector in Zimbabwe. A survey was done with 165 randomly selected accommodation establishments in Zimbabwe. Descriptive analysis was done with the aid of SPSS version 29. The results reveal that the major source of GHG emissions comes from diesel/petrol-powered generators, transport and electricity usage. The findings also disclose that energy management strategies are seen as important in the reduction of GHG.

A Review on Ferry Electrification: A Path towards Sustainable Maritime Transport

This paper reviews research and development efforts for ferries' electrification, focusing on energy supply grids, communication networks, charging systems, and energy storage. It analyzes energy storage technologies, battery charging requirements, shore side infrastructure design, power system architecture, and alternative battery charging stations[1]. The study evaluates the effectiveness of using an electric/hybrid ferry boat for tourist transportation in a real case. The optimal system configuration depends on engine power, energy storage, photovoltaic system sizes, and percentage of hybrid or pure electric usage. The analysis can be replicated to other cases, showing the potential of new technologies for sustainable boating and improving passenger service, especially in terms of comfort[15]. One of the most important steps in lowering greenhouse gas emissions and the marine industry's reliance on fossil fuels is the electrification of ships. This analysis examines the technical developments, difficulties, and environmental effects of switching from conventional fossil fuel-powered ships to electric and hybrid ships. The future of fully electric ships, legislative efforts, and how these advancements relate to international sustainability goals are also covered in the study.

The Effect of Throttle Signal Output Voltage Variation and Load on Current Consumption

Many electric motorcycle users complain about the difficulty in controlling acceleration with precision, especially if they have never ridden a motorized vehicle before. This control difficulty is a result of the characteristics of BLDC motors, which have high torque instantly. While this characteristic is generally considered an advantage, in this discussion it is a disadvantage that can reduce rider comfort when operating an electric motorcycle. When the rider twists the throttle for the first time, the electric motor immediately spins at maximum power. The issues mentioned serve as the background for the this research entitled "The Effect of Throttle Signal Output Voltage Variations and Load on Current Consumption and BLDC Motor Speed in Electric Motorcycles."The method used in this research is an experimental method by varying the load at 60 kg, 70 kg, and 80 kg and three levels of throttle output voltage changes.The results of this study show that changes in load and throttle output voltage affect current consumption and BLDC motor speed. The highest current consumption is achieved when the vehicle is loaded with 80 kg and using mode 1 at 3.5309 A. The lowest current consumption is achieved when the vehicle is loaded with 60 kg and using mode 3 at 1.4403 A. The highest rotational speed is obtained in mode 1 with a 60 kg load at 219.341 rpm. The lowest rotational speed is obtained in mode 3 with an 80 kg load at 141.37 rpm.

Innovative Design of Excess Electricity Usage from an Islanding Micro-Hydroturbine System as an Alternative to Dump Load. A Case Study of Hhaynu River – Mbulu, Tanzania

In rural electrification projects, microhydro turbine systems are suitable for supplying electricity to off-grid areas where water resources are available. The design configuration of micro-hydro turbine systems consists of a water intake system, turbine system and generator system. The power of the water drives the turbine to produce mechanical power that drives the generator to produce electrical power that is supplied to the load demand. In many off-grid and rural areas of developing countries such as Tanzania, where the population density is low, the energy demand is relatively low compared to the power supply at most times of the day. To bridge this gap with the existing microhydro turbine system design, new knowledge on energy storage technologies needs to be studied. This phenomenon is demonstrated in this research project by using hydrogen energy storage. The available excess electricity, instead of being stored into the batteries or sent to dump ballast loads, is used to power the electrolyser system that produces hydrogen gas, which is stored as energy. This new energy utilization approach has been proven to increase the energy storage capacity by eliminating 68.37% of the excess energy needed for the dump load and increasing the excess energy storage from 31.63% for the battery system to 100% for the electrolyser system.

DYNAMIC GENERATIVE RESIDUAL GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR ELECTRICITY THEFT DETECTION

Illegal electricity users pose a significant threat to the economic and security aspects of the power system by illicitly accessing or manipulating electrical resources. With the widespread adoption of Advanced Metering Infrastructure (AMI), researchers have turned to leveraging smart meter data for electricity theft detection. However, existing models rely on methods that model a single electricity load curve and cannot capture the temporal dependencies, periodicity, and underlying features between electricity consumption cycles. This study introduces a novel electricity theft detection method based on dynamic residual graph networks. Innovatively, it proposes a dynamic topological graph construction technique that allows for the real-time updating of adjacency matrices during the training process, thereby effectively capturing the complex relationships in electricity usage patterns. Utilizing the MixHop graph convolutional network, it delves into the temporal sequence dependencies, periodicity, and hidden characteristics within user electricity consumption data. Additionally, to address the issue of model instability caused by scarce theft data, we employ the SMOTE (Synthetic Minority Over-sampling Technique) oversampling technique and enhance overall classification performance by modifying class weights in the loss function. We trained this network architecture on the real SGCC (State Grid Corporation of China) dataset, and the results demonstrate its superiority over other mainstream existing models.

Optimization Of Solar Panel Usage In Grid-Connected Hybrid Energy Systems Using Fuzzy Method

The hybrid grid-connected power generation system combines solar power, wind power, and the PLN grid to meet the electricity demands of facilities such as schools, laboratories, mosques, and kindergartens at MTs Parmiyatu Wassa'adah School. Due to insufficient wind speed below the turbine's operational threshold, wind turbines cannot contribute to electricity generation, making solar power the primary energy source. Solar power capacity is crucial for meeting the electricity needs of these facilities. This study applies the Fuzzy method to analyze the optimal utilization of solar panels in a grid-connected hybrid system for electricity demand. Simulation results indicate three levels of solar panel utilization, with the most optimal performance achieved when school electricity usage is low, and additional loads are minimized.

The Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency

The development of the electric meter system described in this article is titled as Development of a Real-Time Electricity Calculator System Using Machine Learning to Enhance Energy Efficiency. The primary objective is to improve household energy management through prediction and alerts regarding energy usage. The system is designed to collect electricity usage data and various environmental parameters from households, and it employs five machine learning models to identify the best model for this purpose. The chosen model, Support Vector Regression (SVR), is used to predict energy consumption. In the research methodology, the system records real-time energy usage data into a CSV file. The predictive features include temperature, number of occupants, house size, and appliance usage. This data is standardized before being used to train the SVR model. After training, the model’s predictions are evaluated using the Root Mean Square Error (RMSE). The experimental results show that the SVR model effectively predicts electricity consumption, with a normalized RMSE of 57.56 and a cross-validation RMSE of 58.38, indicating the model’s accuracy. The visualizations provide a clear understanding of the overall relationship between actual and predicted values. Household electricity usage prediction enables users to plan energy consumption more efficiently, potentially reducing costs and improving energy efficiency. The development of this system can be applied in various fields, including industry and agriculture, to promote energy conservation and reduce environmental impact.

A CASE STUDY: FUZZY LOGIC BASED DECISION-MAKING SYSTEM FOR ELECTRIC VEHICLE CHARGING

Recently, the use of environmentally friendly electric vehicles instead of traditional internal combustion engine vehicles continues to be widespread due to threats to world life such as global warming and climate change. However, the biggest disadvantages of this technology are the limited range of electric vehicles, long charging times, low number of charging stations, and different charging costs. There is a need for more studies on the problem of finding charging stations, especially for long-distance traveling with electric vehicles. In this paper, a fuzzy logic based decision making system is designed for electric vehicle users to find the most suitable one among the charging stations on a long travelling route. In this study, a traveling route of 1779 km between Izmir and Van provinces in Turkey is selected. The current charging station locations obtained from different charging station companies on this route were processed on Google Earth, and charging stations that were too far from the route were not taken into consideration. A fuzzy logic model was created for 56 charging stations on the route, which performs weight calculation according to the current charging cost and the distance of the station to the normal route. The fuzzy control system is expected to decide on the most appropriate charging station in accordance with the specified rule table, and the results are evaluated.

An integrated S-O-R and B-A-I model to promote electric motorcycles usage rates: a case study in Thailand

An integrated S-O-R and B-A-I model to promote electric motorcycles usage rates: a case study in Thailand

Electricity use in big area additive manufacturing of fiber-reinforced polymer composites

In recent years, additive manufacturing (AM), especially large-format additive manufacturing (LFAM), has gained momentum in the manufacturing industry. While LFAM offers benefits over conventional manufacturing processes, such as minimizing material waste and providing vast geometric freedom, assessing its sustainability remains challenging due to limited data, particularly on energy consumption. Most existing data pertain to small-scale or desktop AM and are not directly applicable to LFAM. In this study, we conducted real-time measurements of electricity usage for a type of LFAM known as big area additive manufacturing (BAAM), which typically uses fiber-reinforced polymer pellets as feedstock. We collected electricity usage data from fifteen printing jobs over two months in an industrial production setting. These data fill the existing gap and can be reused to enhance the community’s understanding of LFAM electricity usage, support further research, and promote sustainable development in advanced manufacturing technologies.

Nationwide monitoring of polychlorinated naphthalenes in soils across South Korea: Spatial distribution, source identification, and health risk assessment.

The production and use of polychlorinated naphthalenes (PCNs) were banned several decades ago, but they continue to be detected due to their persistence in surface environments and ongoing emissions from combustion-related sources. This study presents the first nationwide monitoring data for PCNs in soils collected from 61 sites across South Korea. Industrial sites (mean: 127 pg/g, median: 91.4 pg/g) exhibited higher concentrations of Σ63 PCNs than urban (mean: 53.1 pg/g, median: 50.0 pg/g) and suburban (mean: 52.2 pg/g, median: 23.3 pg/g) sites. Tri- and tetra-CNs were the dominant homologues, with CN-14/24 being the most abundant congeners. The majority of PCN contamination at these sites was attributed to combustion sources and historical emissions from Halowax mixtures. Σ63 PCNs and toxic equivalencies (TEQs) were moderately correlated with the number of companies potentially emitting PCNs, industrial electricity usage, and SO2 concentrations, indicating a significant influence of anthropogenic sources on PCN contamination. The 95th percentile cancer risks for both adults and children across all sites were within the acceptable range (< 10-6) set by the US EPA. However, the higher risks observed for children underscore the importance of ongoing PCN monitoring. This study provides valuable insights into the spatial distribution and human exposure to PCNs in soils across South Korea. Based on these initial nationwide findings, comprehensive monitoring of PCNs and other dioxin-like compounds in industrial areas is recommended.

Design Strategy of Electricity Purchase and Sale Combination Package Based on the Characteristics of Electricity Prosumers in Power System

With the progress in renewable energy and smart grid technologies, electricity users are evolving into prosumers, capable of both consuming and generating electricity through distributed photovoltaic (DPV) systems. Concurrently, the liberalization of the electricity retail market has prompted retailers to design customized electricity packages based on users’ needs and preferences, aiming to enhance service quality, efficiency, and user retention. However, previous studies have not fully addressed the multidimensional characteristics and electricity consumption behaviors that influence package selection. This paper initially dissects user characteristics across three key dimensions: electricity demand preferences, price sensitivity, and risk tolerance. Therefore, leveraging utility functions and autonomous choice behavior models, we propose two innovative electricity purchase and sale combination packages: a fluctuating pricing package and a discount-based pricing package. Furthermore, we introduce the Self-Adaptive Weight and Reverse Learning Particle Swarm Optimization (SAW&amp;RL-PSO) algorithm to address the complexities of these choices. Simulation results indicate that the methodologies presented significantly enhance user benefits and retailer revenues while also effectively managing electricity usage fluctuations and the challenges of integrating large-scale DPV systems into the electrical grid.

How Does the Stock Market Development React to Energy Consumption? Evidence from the ARDL Panel Approach

Abstract This article examines the relationship between stock market development and energy consumption in the Gulf Cooperation Council (GCC) countries using panel data from 1971-2021 and ARDL panel and cross-section techniques. The long-term results show that market capitalization has a significant effect on oil consumption. In contrast, the value of shares traded positively and significantly influences the consumption of oil and electricity. Short-term findings indicate that the overall size of the stock market does not significantly affect electricity usage. This suggests that achieving the desired energy-saving goals may require policies that account for the influence of stock market on energy demand, rather than excluding this factor. Given these findings, policymakers in GCC countries should take into account the intricate and various effects that stock market development have on energy consumption. Accordingly, to reduce energy consumption, policies should not solely focus on energy demand and income relationships. Instead, policies should encourage improved corporate governance practices that incentivize companies to reduce their energy consumption to improve their financial performance and increase their share prices. This can be achieved by encouraging companies to invest in energy-efficient technologies and reducing their reliance on energy-intensive projects. Additionally, policymakers should consider regulating the stock market to promote environmentally responsible investments and reduce the financing of energy-intensive projects.