Electricity Usage Research Articles

Tier-specific energy benchmarking for disparate schools in developing South Africa

The increasing impacts of human-induced climate change in developing countries have spurred government policies, activism, and sustainability research aimed at reducing energy consumption. Understanding the electricity usage of buildings is crucial to cutting carbon emissions and achieving cost savings. This study addresses the challenge of establishing realistic and relevant energy benchmarks for educational institutions in developing countries, specifically focusing on the Western Cape, South Africa. Schools in this region exhibit significant differences in energy intensities but are currently assessed using the same reference standard. A top-down analysis was performed using descriptive statistics to develop energy performance benchmarks tailored to unique patterns of energy consumption in schools. Data from 31 less affluent schools were collected using smart meters to ensure accuracy. The proposed reference benchmarks, ranging from 12 to 37kWh/m2 per year, are significantly lower than the existing 60kWh/m2 per year benchmark, demonstrating substantial potential for energy savings. This nuanced benchmarking approach accounts for seasonal and term variations in energy usage, providing a more accurate comparison across schools. The research introduces a novel, context-sensitive benchmarking method that extends beyond existing standards by incorporating these variations. It underscores the importance of localized benchmarks for achieving school energy efficiency, contributing to environmental preservation and financial savings. The proposed benchmarks offer a robust framework for policymakers, standard bureaus, and education departments to craft energy efficiency policies that drive progress in the education sector. By addressing the unique energy usage patterns of schools, this approach facilitates targeted interventions, leading to improved energy management and sustainability.

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

Finite Element Analysis in The Investigation of Methods to Model AC Resistance in A Coated Conductor

In our electricity dependent and rapidly automated society, the manipulation and usage of electricity to transmit energy is of significant prevalence. In particular, the application of current in high frequency transformers, inductors and motor generators presents a unique efficiency problem: The loss of energy through the spiking of effective resistance intrinsically related to the skin and proximity effects of an AC current. By the usage of Finite Element Analysis (FEA) in the simulative investigation of various variables on AC resistance, this paper seeks to provide insights into how coated conductors might offer advantages over the standard conductor in certain conditions, thus guiding future design and application in high-efficiency electronics. The papers findings reveal that adding a layer of coating with a lower conductivity than the core material could offer reduced energy losses up to the point when the skin depth matches the coating’s thickness. Moreover, the study explored the influence of the coating’s conductivity, suggesting that an increased conductivity of the coating material could further minimize copper losses, albeit with the trade-off of potentially higher costs. The study concludes with a Taylor Series approximation of the relationship between AC resistance and frequency using simulative data.

Forecasting energy consumption with a novel ensemble deep learning framework

Energy baseline development is a central and significant component of an energy management system. In commercial buildings where a wide variety of internal and external variables might impact the electricity usage, having a robust forecasting method is elemental to evaluate and enhance the energy performance over time. Due to extensive progress in data analytics, a number of single prediction methods have been developed recently. Nevertheless, either development of the theoretical study or application of ensemble learning methods to enhance prediction performance and stability find lacking in energy management of commercial sectors. This research proposes a novel heterogeneous ensemble model framework for short-term electricity consumption prediction under the hourly and daily granularity to bridge this research gap. The framework is composed of two levels. The first uses five stand-alone deep learning models including Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) as base models. In contrast, the second level applies the predicted outputs of the base models as feature inputs of k nearest neighbors (KNN), which is a strong model. The Grid Search is also used to search for the key optimal hyper parameters of the base models and the strong model. To demonstrate the developed model's effectiveness, four different space types of real-world educational buildings are used for verification. The extensive database has been setup for this study to collect data from Jan. 2018 to Dec. 2022. The quantitative analysis of the model was conducted in contrast with the base models and prevalent machine learning methods, including artificial neural network (ANN), KNN, support vector regression (SVR), AdaBoost, and linear regression (LR) during the testing phase. The prediction accuracy has improved substantially, with roughly a 95 % reduction in CV-RMSE compared to that of AdaBoost and SVR. Moreover, the results signify the introduced model outperforms the base models and the ML methods in all studied buildings, thereby providing a foundation for developing effective management policies.

Optimized scheduling study of integrated energy system considering user response characteristics

ABSTRACT Integrated Energy Systems (IES) can effectively promote the utilization of renewable energy. Reducing the impact of uncertainties and achieving energy supply-demand balance has always been a key issue in energy scheduling. However, existing research often focuses only on energy prices when designing demand response strategies, neglecting user response characteristics, particularly the influence of environmental temperature and user consumption levels. Therefore, this paper proposes an IES optimization scheduling model that considers user load response characteristics, analyzing changes in user response loads under different environmental and consumption levels. Additionally, consumption levels and environmental parameters are introduced to build an interactive response model. Different incentive measures are designed based on load types to guide users in adjusting their electricity usage behavior, optimizing the supply-demand curve. Furthermore, to address source-load uncertainties, a two-stage robust optimization model is constructed and solved iteratively using the column-and-constraint generation (C&CG) algorithm. The results indicate that considering user response characteristics reduces the peak-to-valley differences in electrical, thermal, and cooling loads by 18.6%, 7.0%, and 13.3% respectively, and reduces total costs by 8.9%. This model can ensure reliable operation while improving the economic efficiency of the system.

Econometric analysis of factors influencing electricity consumption in Spain: Implications for policy and pricing strategies

This study employs a comprehensive database of Spanish homes and econometric modeling to assess the factors influencing electricity consumption in Spain. Using panel data covering the period from 1998 to 2023, we analyzed the average yearly electricity usage across the 18 Spanish regions. Our findings reveal that fluctuations in electricity prices have remained the primary driver of consumption over this period. The price elasticity of demand has been exceptionally high, exceeding levels observed in earlier research and periods. Additionally, factors such as income, hours of sunlight, and temperature fluctuations positively influence electricity usage, albeit to a lesser extent than household and family characteristics (e.g., whether a single parent heads the household or if it includes foreign residents). There is no significant correlation between demand and factors such as the age or employment status of family members. Based on these findings, policy actions that target power pricing (e.g., price-based instruments) are likely to be the most effective in reducing electricity consumption.

Intensive care unit carbon footprint: A bibliometric and document content analysis.

Sustainable low-carbon health systems are among the United Nations Climate Change Conference (COP27) goals. The literature states that 4.9% of greenhouse gas emissions originate from the health sector. This study aimed to quantify and visualize trends, citations, key terms and countries of publications about the carbon footprint of intensive care and review their contents. A bibliometric analysis was conducted on 14 articles selected by searching the SCOPUS database using the keywords 'Environmental sustainability', 'Environmental footprint', 'Carbon footprint' and 'Intensive care unit'. MS Excel was used to create graphs, and VOSviewer was used to perform network analysis. Content analysis methods were used to describe the details in each document. Most articles on intensive care carbon footprint and environmental sustainability were published in 2023 (n = 7, 47%), with the first publication in 2014. The article 'Environmental sustainability in anaesthesia and critical care', from the British Journal of Anaesthesia, was the most cited, with 166 citations. The carbon footprinting studies were conducted in the United Kingdom, Australia, United States and the Netherlands. Minimizing electricity usage and waste generation (reducing, reusing and recycling more) can reduce the carbon footprint of intensive care. The number of studies on the subject was limited, and although none were specifically about nurses, sustainability and environmental impact are relevant topics for all intensive care staff. Nurses and other health care professionals can improve environmental sustainability by better understanding how to reduce the carbon footprint of intensive care units.

Designing a Real-Time DataLogger Using the Internet of Things for a Basic Imple Home Kwh Meter Capable of 1300 VA

Researchers want to make a kWh meter monitoring tool with estimated usage costs so that the tools made can help people to minimize costs in electricity usage. The purpose of this research is the creation of an IoT-based kWh meter monitoring tool that has the ability to read current, voltage, frequency, power factor and power usage that flows and shows the use of costs that have been used in real time with the display results on the LCD and also on the user's smartphone. In the reading results of usage costs (Rp) on the tool and PLN there is also a difference of Rp. 12,000, where the reading results on the tool are also lower than the results of the payment slip provided by PLN. The cost reading error rate on the tool is 4.4% with a reading accuracy rate of 95.6%.

Clustering Method Comparison for Rural Occupant’s Behavior Based on Building Time-Series Energy Data

The purpose of this research is to compare clustering methods and pick up the optimal clustered approach for rural building energy consumption data. Research undertaken so far has mainly focused on solving specific issues when employing the clustered method. This paper concerns Yushan island resident’s time-series electricity usage data as a database for analysis. Fourteen algorithms in five categories were used for cluster analysis of the basic data sets. The result shows that Km_Euclidean and Km_shape present better clustering effects and fitting performance on continuous data than other algorithms, with a high accuracy rate of 67.05% and 65.09%. Km_DTW is applicable to intermittent curves instead of continuous data with a low precision rate of 35.29% for line curves. The final conclusion indicates that the K-means algorithm with Euclidean distance calculation and the k-shape algorithm are the two best clustering algorithms for building time-series energy curves. The deep learning algorithm can not cluster time-series-building electricity usage data under default parameters in high precision.

Electrifying Chemistry: Characterizing TEMPO-Based Electrocatalysts for Isopropanol to Acetone Conversion in Novel Heat Pumps

Heating, ventilation, and air conditioning (HVAC) systems account for more than 40% of the U.S. electricity usage, primarily utilizing vapor compression system (VCS) heat pumps, raising environmental concerns. The phase down of high GWP refrigerants, targeting an 85% reduction by 2035, prompts the exploration of novel heat pump technologies. Electrochemical looping heat pumps (ELHP) show promise by employing redox reactions for fluid compression to replace conventional compression technologies. However, energy-intensive redox interconversion necessitates catalysts to lower activation energy. Inspired by electrosynthesis principles, 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) emerged as a successful homogeneous electrocatalyst for selective isopropanol oxidation to acetone for use in ELHP. HPLC and 13C NMR confirmed remarkable selectivity (~100%) at practical isopropanol concentrations.To enhance TEMPO's catalytic activity, various electron-withdrawing and donating groups were explored. TEMPO-OCH3, among seven derivatives, exhibited superior efficiency with a notable rate constant (6 M-1s-1) and turnover frequency (3.1 s-1). This study innovatively applies concepts from molecular catalysis for the development of efficient catalyst materials.

Analysis of charging tariffs for residential electric vehicle users based on Stackelberg game

Establishing a rational pricing mechanism for electric vehicles (EVs) can incentivize users to engage in charging activities, foster positive consumption behaviors, and indirectly benefit the grid by increasing load demand. Therefore, this paper conducts a Stackelberg game study between the grid and EV users by utilizing real charging and discharging data of EV users in F city. First, the price elasticity of EV users' electricity consumption behavior is analyzed. Second, the Stackelberg game model is constructed, multiple tariff schemes are designed to calculate the equilibrium tariff of the game, and the benefits of the grid and EV users are analyzed in comparison with the tariff policy that will be implemented in F city. Lastly, different seasonal scenarios are constructed to extend the applicability of the research results. It is found that Scheme 3 effectively incentivizes charging load growth, increases cross-subsidies and transmission and distribution fees, and protects the level of charging and discharging costs for residents; furthermore, lowering charging and discharging tariffs during seasons of high price elasticity for residents to increase demand is an optimal strategy for grid pricing.

A fair grid connection cost-sharing model for electricity based on the random forest machine learning method

Developing countries should clarify the dedicated grid connection cost-sharing model when reforming electricity user price policy, which could be learned from developed countries’ experiences. We use the random forest method and monthly data from several developed countries or regions, including the United States and the United Kingdom in 2018–2020, to identify key features that influence decision-makers in choosing dedicated grid connection cost-sharing models and simulate the connection cost-sharing models suitable for various regions of China using provincial data. It provides experience and enlightenment for China and other developing countries to implement the connection price policy reform.

Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China

The electrification of transportation is profoundly reshaping human society and presenting new challenges in terms of travel modes, infrastructure development, and energy supply. Given the potential for large-scale scheduling of electric logistics vehicles (ELVs), it is crucial to thoroughly analyze the usage characteristics and establish reliable models. This study examines the usage patterns and charging behaviors of 29 ELVs in Shenzhen, China, encompassing 34,856 trips and 14,464 charging events. Furthermore, behavior-time probability density models were constructed based on an improved Gaussian mixture model (GMM), which avoids the fitting error caused by misclassification of time series data across time nodes. The article also provides a comprehensive analysis of other statistical findings related to the travel and charging activities of ELVs. The conclusions drawn from this research can serve as valuable references for industries involved in infrastructure construction, power grid management, battery virtual aggregation, and similar sectors.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Algorithm for detecting abnormal electricity usage by customers based on electricity big data and deep learning

Abstract In order to optimize the effect of individuals’ aberrant power utilization detection and reinforce the recognition rate and accuracy of abnormal power consumption detection of electricity use, an aberrant power utilization identification algorithm based on electric power big data and deep learning is proposed. Taking the two-way dialogue between power users and the power company as the basic perspective, real-time monitoring and collection of power consumption dynamics of users is carried out, and the power big data collection framework is designed. The retrieval of aberrant power utilization detection indexes is completed. Based on the deep learning algorithm, the artificial neural network base operation medium is combined to quantitatively solve the aberrant power utilization indexes, completing the design of a user aberrant power utilization detection algorithm. Experimental results show that the accuracy of the designed algorithm is 0.963, the F1 value is 0.985, and the AUC value is 0.942, which can effectively identify the aberrant power utilization and has a better performance of abnormal power consumption identification and detection.

A study on long-term industrial electricity consumption and carbon emission forecasting methods based on TimesNet models

Abstract Aiming at the prediction problem of industrial electricity consumption and carbon emission, a TimesNet model is established to improve the accuracy of industrial electricity consumption and carbon emission prediction. Five significant influencing factors were screened out by grey correlation analysis; based on the energy consumption dataset of an iron and steel enterprise in 2023, the dataset was preprocessed and inputted into the TimesNet model, the prediction results were integrated and summarized, and the prediction results of the DLinear, LightTS, PatchTST, and iTransformer time series prediction models were compared and analyzed. The results show that the predicted values of the TimesNet model fit the actual values, and its mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are 0.31, 0.36, 0.6, and 1.12, respectively, which are better than those of the other models. It proves that the model can predict industrial electricity usage and release of carbon more accurately, providing a practical method for industrial electricity usage and release of carbon prediction.

Research on analysing demand response potential of electricity users based on ISODATA-GMM

Research on analysing demand response potential of electricity users based on ISODATA-GMM

Application of the R Program for CO2 Emission Calculations Based on Secondary Carbon Footprint at MTs Bahrul Ulum

Global warming is partly caused by the increasing amount of greenhouse gas emissions accumulating in the Earth's atmosphere, leading to a rise in surface temperatures over time. Carbon dioxide (CO2) emissions are the main component of greenhouse gases. The largest CO2 emissions from the energy sector come from the use of electricity generated by building activities. This study aims to analyze the CO2 emissions produced from electricity use at MTs Bahrul Ulum. Data collection on electricity usage was conducted by measuring the power consumption of air conditioners, lights, PCs, and laptops used during operational hours, particularly in the MTs Bahrul Ulum school building. CO2 emissions from the electricity consumption of electronic equipment were calculated using emission factors according to the regulations of the Directorate General of Electricity, Ministry of Energy and Mineral Resources (ESDM). The method used is a carbon footprint study. The results show that electricity usage at the location is 6,954.792 kWh/year, and the emissions generated from this electricity usage amount to 8,507.243784 kgCO2/year. This study also developed an application using the R program to facilitate the calculation and analysis of CO2 emissions based on secondary carbon footprints. The application is designed to help users identify and reduce CO2 emissions resulting from daily activities in the school environment.

Challenges of electric vehicle adoption in Indonesia: Revealing the hidden factors affecting purchase intention

Purpose – Recently, electric vehicles (EVs) have seen significant development worldwide. Indonesia has responded by implementing various policies to support the adoption of EVs, which are touted as the future environmentally friendly transportation solution. However, in Indonesia, the purchase intention for EVs still needs to reach expectations, reaching only 14,93% of the target in 2023. This study analyzes the antecedents of benefit factors determining the intention to buy an electric vehicle. This study will fill the gap in earlier research by comprehensively examining the factors that influence EV purchase intention in Indonesia.Design/methodology/approach – This study will fill the gap in earlier research by comprehensively analyzing the factors that influence EV purchase intention in Indonesia. This study will empirically examine the relationship between perceived usefulness, perceived ease of use, perceived risk, EV knowledge, public involvement, and purchase intention, as well as the antecedents of perceived usefulness, perceived ease of use, and perceived risk. This study uses a quantitative method with a survey design to gain an in-depth understanding of the factors influencing the purchase intention of electric vehicles in Indonesia. The survey involved 957 potential electric vehicle users in Indonesia who have never bought an EV but have sufficient interest and knowledge about EVs. The research instrument was a validity and reliability-tested questionnaire used to collect accurate and consistent data.Findings – This study found that although respondents had strong perceptions of the benefits of electric vehicles (perceived usefulness), this did not automatically lead to high purchase intentions. Another surprising finding was that perceived risk also did not directly affect purchase intention. This suggests the presence of other factors inhibiting purchase intention, which need to be identified and addressed.Research limitations/implications – This research contributes significantly to the literature on adopting electric vehicles (EVs). Perceived ease of use has a positive effect on purchase intention. The results of this study indicate that perceived ease of use does not significantly affect the purchase intention of electric vehicles.Practical implications – The findings of this study provide several practical implications for electric vehicle manufacturers, marketers, and policy makers.Originality/value – This research offers a comprehensive approach to understanding the factors that influence electric vehicle (EV) purchasing interest in Indonesia, by including previously overlooked variables such as risk perception, public involvement, and knowledge about EVs. Additionally, this research focuses on the Indonesian context, providing valuable insights into the unique challenges and opportunities in EV adoption in the country, which can form the basis for strategies and policies to promote sustainable transportation.

Participation in demand side response. Are individual energy users interested in this?

DSR is crucial for leveraging user flexibility to integrate renewable energy efficiently, also for smaller users like households, and for utilizing surplus energy from prosumers, highlighting its potential. Public perceptions, habits and responsiveness are crucial to its success. A method to determine the initial state of interest in DSR is a survey. This paper presents the results, obtained on the basis of statistical analysis of responses and averaging of scores according to the Likert scale. The survey was conducted among 400 Polish small electricity users. Poland is an exemplary country where external and internal signals can potentially accelerate building a new customer-supported energy sector. The research found low awareness of DSR among individual users, but a strong interest in the concept. It suggested that incentive schemes are preferred over dynamic tariff systems. In general, energy users show rational behaviour regarding specific DSR options, with financial benefits indicated as the primary motivation and social pressure and fulfilling energy policy objectives were deemed least important. This public perception, however, allows for a wider implementation of DSR. Transparent participation rules are crucial. The results research results can inspire policymakers, energy service providers, and aggregators to tailor DSR solutions for diverse end-users.