Electricity Usage Research Articles

Indicadores de rendimiento energético y ambiental en instalaciones petroleras de producción en Ecuador

The study evaluates the energy and environmental performance of Ecuador's upstream oil facilities between 2015 and 2020, focusing on major extraction blocks. By analyzing data on oil, gas, and water production, fossil fuel consumption, and electricity usage, key performance indicators were developed to assess efficiency and environmental impact. The findings revealed disparities in energy efficiency, with Block 43-ITT being the most efficient at 7.82 kWh per barrel, while Block 57-LB was the least efficient at 31.41 kWh per barrel. The study emphasizes that maturing oil fields require more energy and emit more greenhouse gases, underlining the necessity for sustainable energy practices. EP Petroecuador’s initiatives, such as substituting fuel and integrating renewable electricity, led to a reduction of approximately 540 kTons of CO2 emissions by replacing 163.32 million gallons of diesel with low-carbon fuels and renewable electricity. These findings serve as a baseline for optimizing energy use and reducing emissions, offering valuable insights for policymakers and industry leaders to adopt data-driven strategies that enhance energy efficiency and minimize environmental impact in Ecuador's oil sector.

Environmental Footprints of Yogurt Production: A Case Study of a Small-Scale Manufacturing Process in Sri Lanka

This study investigated the life cycle environmental impacts of yogurt production from cradle to gate at the Pulathisi milk factory in Polonnaruwa, Sri Lanka. The functional unit considered was one kilogram of yogurt produced in the factory using existing technology. The defined system boundary encompassed raw milk production and transportation, machinery fabrication, standardization of milk, and standard unit operations of yogurt production. Material and energy usage data were collected through a series of observations, literature and Ecoinvent 3.7.1 databases. The ReCiPe 2008 method was employed for impact analysis, considering eighteen impact categories with SimaPro software. The inventory analysis included material consumption for machinery fabrication, energy consumption, and emissions for each unit operation in yogurt production. Results were characterized using midpoint indicators, and normalized impacts were calculated. The characterized outcomes of the yogurt production process contributed 2.48 kgCO2eq to climate change, 0.0297 kgSO2eq to acidification, 0.0002 kgPeq to eutrophication, and 6.9199 kg 1,4-DBeq to human toxicity. The study identified hotspots, emphasizing the significant contributions of cow milk production and electricity usage to environmental impacts. Potential mitigations, such as improved waste management in dairy farming and switching to renewable energy sources, are proposed to address these impacts. The normalized impacts provided insights for decision-makers and stakeholders to compare environmental performances and guide sustainable practices in yogurt production. The total impact was recorded as 0.014-person equivalent per year per one kilogram of yogurt.

INFORMATION SYSTEM OF STREET LIGHTING CONTROL IN A SMART CITY

Context. In the context of the rapid development of technologies and the implementation of the concept of smart cities, smart lighting becomes a key element of a sustainable and efficient urban environment. The research covers the analysis of aspects of the use of sensors, intelligent lighting control systems with the help of modern information technologies, in particular such as the Internet of Things. The use of such technologies makes it possible to automate the regulation of lighting intensity depending on external conditions, the movement of people or the time of a day. This contributes to the efficient use of electricity and the reduction of emissions into the atmosphere. Objective. The purpose of the paper is to analyze the procedures for creating an information system as a tool for monitoring and evaluating the level of illumination in a smart city with the aim of improving energy efficiency, safety, comfort and effective lighting management. The implementation of a smart lighting system for Lviv will help improve energy efficiency and community safety. Method. A content analysis of scientific publications was carried out, in which the results of research on the creation of street lighting monitoring systems in real urban environments were presented. The collection and analysis of data on street lighting in the city, such as energy consumption, illumination level, lamp operation schedules, and others, was carried out. Machine learning methods were used to analyze data and predict lighting needs. Using the UML methodology, the conceptual model of the street lighting monitoring information system was developed based on the identified needs and requirements. Results. The role of data processing technologies in creating effective lighting management strategies for optimal use of resources and meeting the needs of citizens is highlighted. The study draws attention to the challenges and opportunities of implementing smart lighting in cities, maximizing the positive impact of smart lighting on modern urban environments. The peculiarities of the development and use of an information system for controlling street lighting in a smart city are analyzed. The potential advantages and limitations of using the developed system are determined. Conclusions. The project on the creation of an information system designed to provide an energy-efficient lighting system in a smart city will contribute to increasing security, particularly, ensuring the safety of the community through integration with security systems, reducing energy consumption, through minimizing the electricity usage in periods when the need for lighting is not necessary. It has been determined that to implement an information system for remote monitoring and lighting control in a smart city, it is advisable to consider the possibility of using a complex lighting control system. Calculations were made on the example of Lviv for the city’s lighting needs. The use of motion sensors to determine the need to turn on lighting was analyzed. A conceptual model of the information system was developed using the object-oriented methodology of the UML notation. The main functionality of the information system is defined.

Electricity Bill Savings from Reduced Household Energy Consumption in Apartment Complexes

Apartments account for 64.6% of all housing units in the Republic of Korea, and most of them receive electricity under a contract, which includes a progressive rate plan. Recently, due to the electrification of energy used in homes and the growing adoption of electric vehicles, electricity consumption in apartment complexes has been gradually increasing. Given the characteristics of the progressive rate system, an increase in electricity usage results in a significant higher rise in electricity bills. Thus, an effective alternative is required to reduce electricity bills for each household. In this paper, the savings in electricity bills achieved by reducing household electricity usage are analyzed from both apartment complex and individual household perspectives, using metering data from 13,332 households. When households are sorted by the amount of savings in descending order, the resulting values are found to follow a negative exponential curve. This indicates that the benefits from reducing electricity usage in households with higher saving are significantly larger compared to other ones. We analyzed bill savings when electricity usage reductions were selectively applied to the top 10%, 20%, and 30% of households with the largest savings. From the results, it is found that the largest savings in electricity bills for households are achieved when usage reductions are applied to the top 10% of households. It is expected that this amount of savings would encourage these households to reduce their electricity consumption. Additionally, it is found that the savings for apartment complexes and the total savings for selected households are not the same, resulting in changes in the bills for households that do not reduce their usage. From the results, it was observed that when the usage reduction of selected households is small or the proportion of households reducing usage is low, the common area charges for non-reducing households tend to increase, leading to higher electricity bills. On the contrary, when the usage reduction of selected households is large or the proportion of households reducing usage is high, the common area charges for non-reducing households tend to decrease, resulting in lower electricity bills.

A clustering method for charging behaviors of electric vehicle users based on an improved k-means algorithm

Abstract The uncertainty of Electric vehicle users’ (EVUs) charging behaviors affect the power grid safety. Therefore, mining the charging behavior characteristics of EVUs can help the power grid to make optimal operation plans previously to smooth the load curve and improve stability. In this regard, a clustering strategy for EVUs is proposed, using an improved K-means algorithm based on the minimum variance theory(MVT). Additionally, the Monte Carlo algorithm is utilized to obtain the simulation results of EVUs. Finally, a numerical simulation based on actual data in China is established to validate the effectiveness and reliability of the proposed cluster algorithm. The results indicate that the proposed cluster algorithm can accurately extract the characteristics of EVUs’ off-on grid behaviors and obtain the accurate daily charging demand representing the main charging rules among EVUs. The proposed algorithm results in a reduction of 69.2% of solution time compared to the traditional K-means algorithm.

Two-level integrated verification evaluation method based on comprehensive weighted assessment in multiple scenarios

The complexity and diversity brought by the distributed architecture of the new generation electric information collection system have deepened the difficulty of constructing evaluation verification index systems and quality evaluation models. Moreover, the presence of differentiated components has made fair and scientific verification challenging. Therefore, leveraging graph neural networks and siamese networks, a integrated construction quality evaluation system based on comprehensive weighted assessment in multiple scenarios was developed. Firstly, a graph neural network was constructed based on terminal data of the branches electricity usage information collection system and the link topology structure. Subsequently, this network was deployed on the headquarters side to directly acquire terminal data and generate mirror network input from the branches data, enabling real-time acquisition of various system operational indicators. Finally, the similarity between the headquarters and branches data was calculated using siamese networks to compute accuracy compensation weights for checking and evaluating various indicators, thereby obtaining comprehensive weighted quality evaluation indicators of the branches new generation electric information collection system. We use three types of services including electricity data collection, load forecasting, and task scheduling as experimental scenarios. The results showed that the multidimensional comprehensive weighted quality assessment combined with accuracy compensation obtained from the siamese network resulted in business construction quality assessment values of 97.92%, 95.95%, and 99.96% in branch. This value is approximately equal to the quality evaluation value of manual work, so the method can effectively verify the construction quality of new systems in the branch.

Heatwaves, Hospitals, and Health System Resilience in England: A Qualitative Assessment (Summer 2019)

Abstract Background Frequent heatwaves challenge healthcare delivery in England, affecting patient safety and staff welfare. This study addresses the observational gap in how healthcare settings adapt to extreme weather. Methods We utilised a qualitative approach, conducting semi-structured interviews with fourteen NHS staff across England to explore experiences during the 2019 summer heatwave. Participants included clinical and non-clinical personnel, providing broad insights into operational challenges and adaptive strategies. Results The 2019 heatwave significantly disrupted service delivery, with variable staff awareness of the Heatwave Plan for England affecting responses. Key issues included managing infection control versus electric fan usage, impacting patient and staff comfort and safety. These conditions also led to an increase in hospital admissions. Conclusions The findings highlight the need for improved workforce training and strategic planning to enhance resilience to heat-related risks. Further research is essential to develop a comprehensive evidence base on the impacts and costs of heatwaves and to assess the effectiveness of adaptation strategies. This will support national health adaptation planning and emergency response. Key messages • Enhanced planning and training are crucial for health system resilience to heatwaves. • More research is needed to understand heatwave impacts and effective responses.

Implementation Of IoT-Based Sense Plug Design On The Prototype Of The FTTH Network Of The UPI Campus Transmission Laboratory In Purwakarta

The increasing demand for energy presents challenges in efficient energy management. This study aims to develop and implement an Internet of Things (IoT)-based energy monitoring system called SENSE Plug, integrated with a Fiber To The Home (FTTH) network at the Universitas Pendidikan Indonesia campus laboratory. The system is designed to monitor real-time electricity consumption, detect voltage anomalies, and manage the electrical usage of connected electronic devices. The research uses an experimental approach by designing and testing the system in the laboratory. Data was collected using the PZEM-004T sensor connected to the NodeMCU ESP8266 microcontroller, with the measurements displayed on an OLED screen and sent to the IoT Cloud for remote monitoring. The system was tested on various electronic devices to measure voltage, current, active power, and power factor. The results indicate that the SENSE Plug system achieved high accuracy, with an average error of 0.44% for voltage and 0.41% for current. The system can also detect voltage anomalies and automatically disconnect the power supply to prevent damage to devices. In conclusion, SENSE Plug contributes to improving energy efficiency and offers a solution applicable to smart labs and other building environments

Optimizing Compressed Air Operations for Electrical Energy Savings: A Case Study in Pharmaceutical Packaging Manufacturing

This study in pharmaceutical packaging manufacturing focuses on improving compressed air efficiency through targeted strategies at both the source and user levels by establishing a baseline to analyze energy consumption patterns. Key measures, including minimizing air leaks, adjusting pressure, and optimizing compressor performance, aim to achieve a 20-50% increase in efficiency, thereby supporting environmental sustainability. The User Point and Source Point approaches are expected to lower Specific Power Consumption (SPC), with data collected from December 2020 to May 2022 providing insights into potential energy savings. Establishing this baseline, based on machine runtime and productivity, offers a solid foundation for evaluation. Results show a 23% reduction in compressor electricity usage and a 7-8% decrease in compressed air consumption. A structured improvement process and strong collaboration between engineering and management are essential for enhancing productivity and achieving sustainable energy efficiency in the industrial sector.

Sustainable Horizons: Exploring Attitudes and Beliefs on Electric Aircraft Usage in General Aviation

This research was conducted to better understand the perceptions surrounding electrically powered aircraft in the general aviation (GA) sector. The study employed survey methodologies to collect responses from ( N = 780) participants, with an emphasis on data from collegiate aviation students as well as a broad subset of the GA community. The study focused on factors such as generational differences, gender, the impact of flight training, and prior exposure to electric or hybrid cars, and how these factors influence the aviation industry’s sustainable transformation. The results indicate a significant and positive correlation between attitudes toward electric aircraft and prior exposure to or ownership of electric or hybrid cars. Generational differences, gender-based variations, and the impact of flight training on willingness to pay for electric aircraft utilization also had significant relationships with attitudes toward electric aircraft. The findings provide valuable insights for industry stakeholders and policymakers in promoting sustainable aviation practices, and complements prior research into the feasibility of electric flight.

Natural resource rent, green finance, and CO2 emissions from the industrial sector

This study investigates the effect of natural resource rents and green finance on industrial sustainability in 20 OECD countries from 2010 to 2020, using the CCEMG-ARDL approach. Findings reveal that a 1% increase in green finance reduces CO2 emissions by 0.43% in the short term and 0.27% in the long term, highlighting its role in supporting environmentally friendly investments. In contrast, a 1% rise in fossil fuel rents increases CO2 emissions by 0.29% short-term and 0.05% long-term, showing the pollutive impact of fossil fuel profits. Additionally, a 1% increase in electricity usage raises CO2 emissions by 0.41% short-term and 0.16% long-term, stressing the need for cleaner energy. Industry-specific patents show limited alignment with sustainability goals. Policy measures should target resource rent management, boost green finance, and encourage sustainable practices to promote industrial greening.

Typical Scenario Load Identification Based on Feature Fusion and Transfer Learning

Background: The electricity demand is continuously increasing. However, various institutions, enterprises, and individuals exhibit many irregularities in their electricity usage, leading to significant wastage of electricity. To achieve effective energy management, researchers are attempting to analyze and regulate users' electricity demands by monitoring their load usage through Non- Intrusive Load Monitoring (NILM) technology. The accuracy of load identification in this technology will greatly impact the results of load monitoring. Although there are currently many articles and patents related to NILM, they utilize a large amount of computational resources and require high sampling rates from devices, yet the results are still unsatisfactory. Therefore, it is necessary to improve the accuracy of load identification in data with relatively low sampling frequencies. Objective: To improve the accuracy of load identification with low sampling frequency data, this paper proposes a typical scenario load identification method based on feature fusion and transfer learning. Methods: This method adopts the fusion of current and power factor angles to provide abundant identification information for NILM, effectively reducing the situation of single-feature overlap of different loads. By inputting the fused feature data into GoogLeNet and utilizing transfer learning for training, not only is the accuracy improved, but also the training time and the requirement for the sampling rate of training data are greatly reduced. In addition, selecting typical scenario loads can monitor loads in a targeted manner, reduce the waste of computing resources caused by irrelevant loads, and more effectively guide electricity usage strategies. Results: The proposed load identification method was tested on the low sampling frequency dataset used in this paper. It achieved an overall load identification accuracy of 94.61% across three scenarios, improving accuracy by 3% to 7% compared to other models. Conclusion: The simulation results indicate that this method achieves high load identification accuracy at low sampling frequencies. It also exhibits good generalization ability. This method not only reduces the performance requirements for monitoring equipment but also enhances monitoring efficiency.

Modeling of Carbon Footprint Emissions in Sugarcane Production Using System Dynamics

Objective: This study proposes a model aimed at estimating and reducing carbon emissions in sugarcane cultivation and juice extraction, designed for potential application in real-world systems. Method: The model grounded in a hypothetical case study of a 60-hectare sugarcane plantation in a warm climate with a 7-month growth cycle and a three-year simulation period, focuses on estimate and evaluate mitigation scenarios to decrease emissions from fuel and electricity usage in activities such as plowing, sowing, harvesting, and irrigation. Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation. Results and Discussion: Utilizing Vensim PLE, a System Dynamics model that combine event and dynamic simulation estimated a 3060.81 tons CO2 equivalent carbon footprint. Two scenarios aimed at footprint reduction were tested: substituting electrical energy with solar power reduced the footprint by 86% to 450.092 tons, demonstrating clean energy's efficacy. Conversely, replacing an industrial mill with the traditional trapiche technique for juice extraction yielded a mere 1% reduction, indicating its ineffectiveness for real-world application. Research Implications: This study emphasizes the development of a simulation model based on system dynamics to estimate the carbon footprint emissions in sugar cane production considering its cultivation and juice extraction. The results support that through simulations, it is possible to determine the factors that influence the generation of carbon dioxide, offering a broader vision to establish and implement mitigation measures such as adopting clean energies and reducing fuel consumption. Originality/Value: This research contributes to the understanding of the application of system dynamics by using software such as Vensim PLE to estimate the carbon footprint emissions in sugar cane production, considering its cultivation and juice extraction. The results found through simulations suggest the implementation of mitigation measures such as the adoption of clean energies and the reduction of fuel consumption.

Renewable energy as an auxiliary to heat pumps: Performance evaluation of hybrid solar-geothermal-systems

The aim of this study is to combine renewable energy sources with heat pumps so that the usage of electricity needed to operate heat pumps is minimized along with associated fuel combustion. The aforementioned objective leads to reduce the energy consumption, the operational cost and the environmental impact of the heat pump. To minimize the usage of electricity, it is proposed that the heat pump (HP) system is combined by two renewable energy systems, a Solar Air Heater (SAH) and a Geothermal Well Water (G). To enrich this study, five prospective combinations of Heat Pump (HP), Solar Air Heater (SAH) placed Upstream (U) and Downstream (D) of the condenser, and Geothermal Water Well (G) were investigated. Hereafter, these five combinations are referred as HP-G, HP-S-U, HP-S-D, HP-G-S-U and HP-G-S-D. The thermal modeling of the aforementioned combinations in addition to baseline HP were developed and examined using an in-house computational code. To ensure that the input data used in the computational code are reliable, experiments were conducted to validate that the geothermal water temperature is higher than the ambient temperature in winter, in addition to confirming the analytical thermal modeling of the solar air heater. Numerical analyses and associated parametric studies revealed that the combination of Solar Air Heater (SAH) and a Geothermal Well Water (G) can efficiently increase the performance of the system by reducing the power needed to operate the compressor of HP. The gain in COP was found to be 48, 43, 81, 105 and 191 % for HP-G, HP-S-U, HP-S-D, HP-G-S-U and HP-G-S-D respectively. In addition, results revealed that the most efficient system is the (HP-G-S-D) for all simulated conditions and assumptions with a gain in COP that can reach up to 191 % in comparison to the baseline heat pump system.

Distortion corrected kernel density estimator on Riemannian manifolds

Manifold learning obtains a low-dimensional representation of an underlying Riemannian manifold supporting high-dimensional data. Kernel density estimates of the low-dimensional embedding with a fixed bandwidth fail to account for the way manifold learning algorithms distort the geometry of the Riemannian manifold. We propose a novel distortion-corrected kernel density estimator (DC-KDE) for any manifold learning embedding, with a bandwidth that depends on the estimated Riemannian metric at each data point. Exploiting the geometric information of the manifold leads to more accurate density estimation, which subsequently could be used for anomaly detection. To compare our proposed estimator with a fixed-bandwidth kernel density estimator, we run two simulations including one with data lying in a 100 dimensional ambient space. We demonstrate that the proposed DC-KDE improves the density estimates as long as the manifold learning embedding is of sufficient quality, and has higher rank correlations with the true manifold density. Further simulation results are provided via a supplementary R shiny app. The proposed method is applied to density estimation in statistical manifolds of electricity usage with the Irish smart meter data.

Analysis and control of demand response in smart grids: An evolutionary game method

As an effective strategy for load management in smart grids, demand response establishes a bidirectional connection between the electricity supplier and users. Based on the networked evolutionary game theory, this paper studies the demand-response issue for a class of smart grids by using the semi-tensor product of matrices. The paper proceeds as follows. (i) Considering the dynamic interactions between the supplier and users, the demand response is modeled as a heterogeneous networked evolutionary game and is expressed as dynamical form by semi-tensor product. (ii) A sufficient and necessary condition is provided to verify the convergence to a fixed point of the considered system. (iii) A feedback controller is designed to ensure the system electricity consumption and price to maintain at a desired level. Finally, an example is presented to illustrate the feasibility of the proposed method.

Heterogeneity in electric taxi charging behavior: Association with travel service characteristics

A comprehensive understanding of charging behaviors among electric vehicle users is crucial for advancing green transportation and deploying effective charging infrastructure. This study conducted large-scale empirical research using data from electric taxi fleets in Shenzhen to explore the heterogeneity of charging behaviors among taxi drivers. The study hypothesized that distinct charging patterns exist within the electric taxi fleet, impacting fleet-wide fluctuations and repetitions of charging and service activities. Employing a covariate-enhanced latent profile analysis model, we examined unique charging patterns within the fleet and investigated relationships between taxi service attributes and charging behavior heterogeneity. Fleet-wide diurnal fluctuations, daily repetitions, and subgroup-specific charging patterns were identified. At the micro level, operational activity sequence similarity and between-group diversity were assessed. The findings offer valuable insights for policymakers and stakeholders involved in promoting green transportation and optimizing charging infrastructure.

Monitoring system for electrical load usage characteristics in electrical engineering building

Electrical energy is becoming increasingly essential for educational activities in electrical engineering facilities, as these activities include the use of electrical equipment. The quantity of electrical loads in buildings escalates annually, resulting in increased demand on existing lines and the original design of building construction. Nonetheless, the positioning and allocation of the load within the panel do not align with the augmentation of the load. Consequently, the load becomes uneven across all three stages. Phase T is experiencing an overload, while Phase R has a lesser burden compared to Phases S and T. The maximum current in Phase T is 36.3 A; the imbalance is excessive, leading to a neutral current of 25.2 A. This is perilous since the present value at the neutral point ought to be zero; if the load remains unregulated, it will lead to a contact voltage in electrical apparatus. The utilization of cables is unsuitable due to the substantial connected load, while the cable diameter is merely 2.5 mm², capable of conducting a maximum current of 21 A. Therefore, it is imperative to reassess the electrical installation in the engineering building to avert short circuit disruptions.

Game theoretic operation optimization of photovoltaic storage charging station considering uncertainty and carbon trading

With the advancement of energy conservation and emission reduction efforts, the orderly charging of electric vehicles and the operation of photovoltaic-storage-charging stations associated with electric vehicles have become increasingly important topics. This study constructs an optimization model for the operation of stations under the synergy of electricity and carbon markets from a game theory perspective. Firstly, Latin hypercube sampling and Monte Carlo sampling are employed to handle the uncertainties in photovoltaic output and the stochastic nature of electric vehicles charging. Secondly, a ladder-type carbon trading mechanism is introduced, and a charging optimization model based on Stackelberg game theory is developed to describe the benefit interaction between charging stations and electric vehicles users. In this model, the upper level represents the charging station operator aiming to maximize joint electricity and carbon revenue while minimizing load fluctuations, whereas the lower level represents electric vehicles users aiming to maximize consumer surplus. Finally, a genetic algorithm nested with mixed-integer linear programming is used to solve the optimization model. The simulation results validate the model's effectiveness and superiority. The results indicate that, compared to centralized optimization methods, the Stackelberg game mechanism can increase consumer surplus by 119.40 %, reduce carbon emissions by 217.92 %, and achieve a win-win situation for both parties. Compared to a fixed carbon trading value, the ladder-type carbon trading mechanism can reduce carbon emissions by 23.84 % and smooth load fluctuations.

Electricity user behavior analysis and marketing strategy based on internet of things and big data

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.