Electricity Usage Research Articles

Socioeconomic indicators and their influence on the adoption of renewable energy technologies in rural Malawi

Purpose This study aims to examine the impact of socioeconomic factors on electricity usage and assess the feasibility of implementing a mini-grid system in Kasangazi, Malawi. The primary aim is to understand the community’s current and potential utilisation of electrical equipment. Design/methodology/approach A mixed-methods approach was used to collect quantitative and qualitative data. Information was gathered through structured questionnaires, and energy audits were conducted among 87 randomly selected households from 28 Kasangazi communities. Data analysis relied on descriptive statistics using IBM SPSS version 28. Findings The study indicates that every household in Kasangazi uses non-renewable energy sources: 60 households use disposable batteries for lighting, 20 for radios and all use firewood, freely sourced from local forests, for cooking and heating water. The study shows that firewood is the community’s preferred energy source, illustrating the challenges faced in the fight against deforestation. Most household income comes from farming, with smaller contributions from businesses, employment and family remittances. Access to higher education is scarce, with only one out of 349 family members receiving tertiary education. Despite the constraints of low education levels and income, there is a demand for larger electrical appliances such as stoves and refrigerators. This underscores the need for mini-grid solutions, even in less technologically advanced, agriculture-dependent communities. Originality/value This study underscores that in Sub-Saharan Africa, factors like household size, income and education levels do not significantly influence the electricity demand but should be taken as part of the fundamental human rights. Rural populations express a desire for electricity due to the convenience it offers, particularly for appliances like refrigerators and stoves. Mini-grids emerge as a viable alternative in regions where grid electricity provision is challenging. It is concluded from this paper that the issue of using renewable energy should not only be taken for environmental preservation but also to promote energy access, augmenting efforts in supplying electricity to the remotest parts of the country.

Leveraging the Inflation Reduction Act to Achieve 80x30 in the US Electricity Sector

Leveraging the Inflation Reduction Act to Achieve 80x30 in the US Electricity Sector

INTERACTIVE EDUCATION ON SUSTAINABLE USE OF ELECTRICAL DEVICES

Education in GREEN electrical energy and production usage is essential for young people. Every consumer can do a lot if they use electrical devices wisely. This paper will present the design, intention and use of a didactical learning exhibit for middle and high school students during technical days on faculty and faculty promotions and for educational purposes. Our main goal is to introduce students to how some everyday electrical devices work, how much energy they use and how they can save as users. The most significant emphasis will be electromagnetic devices such as elevators and devices we use daily. We will use a professional approach and design for that purpose, but we will focus on presenting it on an interactive touchscreen and small examples of those devices. The content will be in graded expertise levels, from simple picture and animation calculations to the advanced picture and mathematical model presentations. The exhibit will be designed modularly so that content or presentation can change quickly and be made from recycled or sustainable materials as much as possible. KEYWORDS:Sustainable; Educational; Electricity; Electromagnetic devices; Interactive; Elevator; Exhibit

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

Voltage drop comparison in Lead-acid and Lithium-ion batteries on electric scooters

This article compares the voltage drop on Lead-acid and Lithium-ion batteries used as an energy source for electric scooters. An experiment was conducted with five electric scooter users weighing 60.25 Kg, 70.60 Kg, 83.20 Kg, 95.40 Kg, and 103.75 kg to obtain data on voltage drop. Each user of this electric scooter carried out three experiments by circling about 1.07 Km at the Politeknik Negeri Bali area. This study proves that the voltage drop on the Lead-acid and Lithium-Ion batteries has a linear relationship to the weight of an electric scooter user with a 36V 400-watt BLDC motor load. The linear equation has an intercept value of -1.36766 and a slope value of 0.04791 for the Lead-acid battery. In contrast, the linear equation has an intercept value of -2.47064 and a slope value of 0.05417 for Lithium ion batteries. The average voltage drop across lead acid is 25% higher than that across Lithium-ion.

Operational performance of PCM embedded radiant chilled ceiling using a rule-based control strategy

The increasing energy demand for space cooling, projected to triple by mid-century and surpassing a quarter of today's global electricity usage, underscores the urgent need for innovative, more energy-efficient air-conditioning technologies. The research introduces phase change material (PCM) embedded radiant cooling (PCM-RCC) as a promising solution, offering enhanced energy efficiency and indoor environmental quality. Despite its potential, PCM-RCC remains in the developmental stage, requiring further research to fully unlock its benefits. This work aims to analyse the operational performance of PCM-RCC and develop an advanced rule-based control strategy to enhance its efficiency further. The analysis was conducted using a validated PCM-RCC transient system simulation model. To quantify the advantages of PCM-RCC over typical radiant cooling and evaluate the effectiveness of the proposed control strategy, two reference cases are defined: (1) basic-controlled PCM-RCC, and (2) radiant cooling without PCM. Results reveal that PCM-RCC with advanced control demonstrates higher load flexibility (∼93% off-peak time operation), 12% less electricity usage, a 5% coefficient of performance improvement, and a 30% decrease in operational costs compared to radiant cooling. Furthermore, the proposed system shows 9% lower energy usage with a 20% improvement in thermal comfort compared to basic-controlled PCM-RCC.

Decomposition and Driving Factors of Total Factor Productivity of Food Crops in the Yellow River Basin, China

The urgency of enhancing agricultural productivity within the Yellow River Basin cannot be overstated, given its critical role in ensuring food security amidst the challenges posed by climate change, natural disasters, and the increasing demand for food crops. Utilizing panel data from nine provinces within the Yellow River Basin for the period 2001 to 2020, this study examines the temporal characteristics and spatial distribution of Total Factor Productivity (TFP) for key grain crops—namely wheat, corn, and soybean—through the application of the Malmquist index which can be decomposed through the DEA-Malmquist index methodology. The empirical results demonstrate that TFP growth rates for these crops have exhibited significant phase variations, with soybean recording the highest TFP growth rate in the basin. Additionally, this study underscores marked regional disparities in soybean productivity. TFP decomposition reveals that the primary drivers of TFP improvement across these crops are attributed to technical progress, with gains in overall technical efficiency largely due to scale efficiency enhancements, whereas pure technical efficiency has shown limited progress. Regional analysis indicates that Inner Mongolia leads in TFP growth for all crops, while Ningxia, Sichuan, and Shaanxi lag behind in wheat, corn, and soybean. Additionally, our analysis delineates natural disasters as a significant barrier to Total Factor Productivity (TFP), notably obstructing technological advancements in wheat cultivation. The investigation further reveals a positive relationship between regional per capita income and the growth of wheat TFP, in contrast to a negative relationship with the TFP growth of corn and soybeans. Moreover, investing in agriculture, forestry, water management, and road infrastructure supports the growth of wheat TFP, while urbanization levels pose constraints. Conclusively, an uptick in annual rural electricity usage, along with improved per capita postal and telecommunication services, exerts a favorable influence on TFP for corn and soybeans.

Energy-saving techniques in urban aquaponics farms by optimizing equipment operating scheme

Urban aquaponics farms, by integrating aquaculture with hydroponic vegetable crops, are innovative and sustainable food production alternatives for urban regions with limited access to agricultural land and water resources. The equipment applied to aquaponics farms including LED lamps, air conditioning, osmotic systems, fertigation units, and pumps consume vast amounts of energy. By characterizing the non-schedulable and schedulable assets, this study proposes an optimized load scheduling approach that determines the equipment operating scheme with minimized operating costs and renewable energy loss. With the assumptions made, the proposed algorithm reduces the overall farm electricity usage and makes vertical farms more energy efficient.

Bitcoin is an Environmental Disaster. Is the Indictment Distorted?

The literature is inundated with the claim that Bitcoin pollutes the environment. While the assertion is irrefutable, the unsettled issue is whether the indictment of environmental disaster is disproportionate or distorted. This is an empirical question; this paper evaluates it via two econometric methods. First, accepting carbon dioxide (CO2) emission as a proxy for environmental pollution, the paper quantifies the elasticity of CO2 emission in relation to electricity consumption in Bitcoin production. The results reveal that, while Bitcoin production based on conventional electricity is inelastic, carbon emission responsiveness to fossil fuel is significant. A 1% increase in Bitcoin’s usage of coal-generated electricity leads to a 1.64% surge in CO2 emission. Second, the study applies the error correction model to show that some electricity consumption shocks emanate from global coal prices driven by economic factors beyond Bitcoin’s control. This raises the question of whether Bitcoin should shoulder the entire blame for the 1.64% pollution responsiveness. Therefore, the study makes two important contributions. First, Bitcoin’s pollution impact varies according to timespan and electricity source. Second, the intensity of carbon emission from electricity consumption is aggravated by external market factors beyond Bitcoin’s control. The findings should inform policymakers and enlighten environmental advocates.

Design and Construction of an Internet of Things Based Electrical Power Usage Monitoring System

Design and Construction of an Internet of Things Based Electrical Power Usage Monitoring System

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

Xylem: An Energy-Efficient, Globally-Redistributive, Financial Infrastructure using Proof-by-Location

The Proof-of-Work algorithm that underlies Bitcoin, Ethereum w 1 and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This paper describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency “Notaries” around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. In this paper, we demonstrate that this platform can scale to more than 3.9 trillion transactions per year (more than triple the number of digital payments per year currently occurring). We show a reduction of electricity usage per transaction of 99.9999914% compared to Bitcoin, 99.999905% compared to Ethereum w , 99.83% compared to Ethereum, and 95.9% compared to the Visa financial services company. We demonstrate that this platform would have a redistributive rather than consolidatory effect on wealth compared to any of these platforms, leading to a source of income for more than 1 billion people around the world, including more than 110 million in the bottom 10-20th percentile by income, with income for that group equivalent to 8.8 million full-time jobs. Finally, this currency provides a positive, non-compulsory mechanism for shaping human habitation patterns in ways that can slow global biodiversity loss and enable ecological restoration. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms.

What is the environmental impact of a blood transfusion? A life cycle assessment of transfusion services across England.

Healthcare activities significantly contribute to greenhouse gas (GHG) emissions. Blood transfusions require complex, interlinked processes to collect, manufacture, and supply. Their contribution to healthcare emissions and avenues for mitigation is unknown. We performed a life cycle assessment (LCA) for red blood cell (RBC) transfusions across England where 1.36 million units are transfused annually. We defined the process flow with seven categories: donation, transportation, manufacturing, testing, stockholding, hospital transfusion, and disposal. We used direct measurements, manufacturer data, bioengineering databases, and surveys to assess electrical power usage, embodied carbon in disposable materials and reagents, and direct emissions through transportation, refrigerant leakage, and disposal. The central estimate of carbon footprint per unit of RBC transfused was 7.56 kg CO2 equivalent (CO2eq). The largest contribution was from transportation (2.8 kg CO2eq, 36% of total). The second largest was from hospital transfusion processes (1.9 kg CO2eq, 26%), driven mostly by refrigeration. The third largest was donation (1.3 kg CO2eq, 17%) due to the plastic blood packs. Total emissions from RBC transfusion are ~10.3 million kg CO2eq/year. This is the first study to estimate GHG emissions attributable to RBC transfusion, quantifying the contributions of each stage of the process. Primary areas for mitigation may include electric vehicles for the blood service fleet, improving the energy efficiency of refrigeration, using renewable sources of electricity, changing the plastic of blood packs, and using methods of disposal other than incineration.

The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach

Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households’ electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Environmental sustainability assessment of biodegradable bio-based poly(3-hydroxybutyrate-co-3-hydroxyvalerate) from agro-residues: Production and end-of-life scenarios

In the context of a circular bio-based economy, more public attention has been paid to the environmental sustainability of biodegradable bio-based plastics, particularly plastics produced using emerging biotechnologies, e.g. poly(3-hydroxybutyrate-co-3-hydroxyvalerate) or PHBV. However, this has not been thoroughly investigated in the literature. Therefore, this study aimed to address three aspects regarding the environmental impact of PHBV-based plastic: (i) the potential environmental benefits of scaling up pellet production from pilot to industrial scale and the environmental hotspots at each scale, (ii) the most favourable end-of-life (EOL) scenario for PHBV, and (iii) the environmental performance of PHBV compared to benchmark materials considering both the pellet production and EOL stages. Life cycle assessment (LCA) was implemented using Cumulative Exergy Extraction from the Natural Environment (CEENE) and Environmental Footprint (EF) methods. The results show that, firstly, when upscaling the PHBV pellet production from pilot to industrial scale, a significant environmental benefit can be achieved by reducing electricity and nutrient usage, together with the implementation of better practices such as recycling effluent for diluting feedstock. Moreover, from the circularity perspective, mechanical recycling might be the most favourable EOL scenario for short-life PHBV-based products, using the carbon neutrality approach, as the material remains recycled and hence environmental credits are achieved by substituting recyclates for virgin raw materials. Lastly, PHBV can be environmentally beneficial equal to or even to some extent greater than common bio- and fossil-based plastics produced with well-established technologies. Besides methodological choices, feedstock source and technology specifications (e.g. pure or mixed microbial cultures) were also identified as significant factors contributing to the variations in LCA of (bio)plastics; therefore, transparency in reporting these factors, along with consistency in implementing the methodologies, is crucial for conducting a meaningful comparative LCA.

An Overview of Electricity Consumption in Europe: Models for Prediction of the Electricity Usage for Heating and Cooling

Although aggregate electricity consumption provides valuable information for market analysis, demand composition, including industrial, residential, illumination, and other uses, and special days, such as national or religious holidays and annual industrial shutdowns, differ for each country. This paper analyzes the hourly electricity consumption of European countries in the European Transmission System Operation for Electricity (ENTSO-E) grid from 2006 to 2018. We propose an outlier detection method to identify special days and a modulated Fourier Series Expansion model to determine the breakdown of industrial versus household consumption and heating versus cooling consumption. The proposed outlier detection method uses the time series for each hour and checks whether a day has more than a threshold number of hours with exceptional electricity consumption levels. The proposed demand prediction model has a 3% average error when electricity usage for heating is not dominant. It also allows country classification based on consumption patterns to efficiently manage regional or country-based electricity markets.

Evaluating Carbon-Emission Efficiency in China’s Construction Industry: An SBM-Model Analysis of Interprovincial Building Heating

In the pursuit of China’s ambitious carbon neutrality goals, optimizing carbon-emission efficiency within the construction sector, a significant emitter, becomes critical. This study employs a super-Slacks-Based Measure (SBM) model and a Tobit regression model to analyze buildings’ heating-related carbon emissions across China, considering urban population density, electricity usage, and building energy consumption and the influencing factors that cause differences in carbon-emission efficiency difference. The results of this study show that the average building carbon-emission efficiency of 30 provinces in China is 0.789; carbon-emission efficiency is 0.89 in the south, higher than 0.69 in the north. After excluding centralized heating emissions, the value of buildings’ carbon-emission efficiency in the northern provinces increases by 0.01, of which the buildings’ carbon-emission efficiency in Jilin Province and Ningxia Hui Autonomous Region shows positive growth, respectively, by 0.12 and 0.17. In terms of influencing factors, there is a significant positive correlation between the scientific and technological levels, the regional economic scale, and carbon-emission efficiency; however, government intervention in the economy has a negative correlation with carbon-emission efficiency. Renewable energy utilization and green-policy adoption emerge as pivotal in enhancing efficiency. The contribution of this study is to underscore the necessity of fostering renewable energy, refining energy-consumption structures, and implementing green strategies to augment buildings’ heating-related carbon-emission efficiency.

Sensitivity analysis and comparative assessment of novel hybridized boosting method for forecasting the power consumption

This research focuses on the crucial task of accurately forecasting electricity consumption, a key concern in modern societies where electricity is essential for industries, healthcare, and transportation. The study explores the complex factors influencing electricity usage, aiming to improve the accuracy of consumption forecasts. Recognizing the limitations of current forecasting techniques, which often fail to account for the detailed interplay of variables affecting consumption, it introduces innovative methods to address these challenges. By employing sensitivity analysis and advanced hybrid boosting algorithms, such as LightGBM and CatBoost optimized with Arithmetic Optimization and Fruit Fly Optimization Algorithms, the research analyzes time-series data on electricity use, weather conditions, and usage timing across three power distribution networks. The sensitivity analysis reveals the significant impact of customer behavior and temperature on consumption patterns. The predictive models, especially the optimized LightGBM, demonstrate remarkable accuracy, with rates of 0.9996 and 0.9968 for training and testing datasets, respectively. These results highlight the effectiveness of the proposed methodologies in providing accurate and reliable forecasts of electricity demand, offering valuable insights for the energy sector to manage resources more efficiently and meet consumption needs effectively.

Next-Generation Billing Systems: The Smart eBill Revolution

Efficient monitoring of electrical consumption is essential for promoting energy conservation and optimizing electricity usage. This project introduces a "Smart EBill System" using current and voltage sensors, along with an LCD display for real-time energy consumption tracking. The system aims to provide users with instant information on their electricity usage, helping them make informed decisions and promoting energy efficiency. This integration of sensors and a display offers an intelligent solution for smart and user-friendly electricity bill monitoring. In today's digital age, electronic billing systems have become a cornerstone of modern business operations, offering convenience, efficiency, and sustainability compared to traditional paper-based billing methods. However, as electronic billing systems evolve, there is a growing need for smarter, more intuitive solutions that enhance user experience, streamline processes, and leverage emerging technologies. This paper presents Smart eBill, a next-generation electronic billing system designed to address these evolving needs. Smart eBill integrates advanced features such as artificial intelligence, machine learning, and data analytics to revolutionize the billing experience for businesses and consumers alike. The system employs AI algorithms to automate billing processes, including invoice generation, delivery, and payment reconciliation, reducing manual errors and administrative overhead. Furthermore, Smart eBill leverages machine learning techniques to analyze billing data and provide personalized insights and recommendations to users. By understanding user preferences, spending patterns, and historical data, the system offers tailored suggestions for optimizing billing arrangements, reducing costs, and improving financial management.