Analysis Of Electricity Consumption Research Articles

Impacts of microwaves on the pectin extraction from apple pomace: Technological properties in structuring of hydrogels

The valorization of by-products eliminates the risks of economic barriers and reduces the overall amount of pollution and the carbon footprint of the products. In this context, conventional heating extraction (CE) and microwave-assisted extraction (MAE) were compared to extract pectin from apple pomace. The effects of temperature on MAE were evaluated based on the resulting properties obtained in subsequent pectin hydrogels. A pectin yield of 10.6 ± 0.3% was obtained by MAE with 5 min extraction, while CE showed a yield of 9.5 ± 0.3% after 2 h. The pectin extracted by both methods had a low methoxyl content, indicating a low degree of esterification. The galacturonic acid content was greater than 60% for most samples. Zeta potential analysis indicated the ability of the pectin to form stable gels. According to the X-ray diffractograms, increasing the extraction temperature led to a decrease in the crystallinity of the pectin structure. Rheological tests showed increased storage modulus as the temperature increased by MAE from 80 to 100 °C for the pectin-based hydrogel. The water holding capacity was greater than 50% for all hydrogels. Furthermore, an analysis of electrical consumption and energy costs highlighted the economic advantage of using microwave heating technology to extract pectin.

Univariate vs multivariate predictability analysis of electricity consumption by LSTM approach: An empirical evaluation

Precise prediction of EC is crucial for planning, managing, and cost-effective operation of power grids, as it is a time series problem. In recent years, numerous studies have analyzed the behavior and quality of time series forecasts in different areas, including the EC. New models or variants of traditional algorithms have also been proposed, usually taking advantage of the increasing amount of data available and of today's computing power, such as LSTMs. One of the most important characteristics in the selection of a forecasting method is the number of variables that must be taken into account in the prediction of the time series since most of these variables are subject to external influences. EC is dependent on a number of external factors, such as climatic factors, economic factors, and the spot price of electricity. The EC dataset may be either univariate or multivariate. When dealing with univariate time series data, it's important to utilize specific methods that take only the historical values of the variable into consideration for accurately estimating its pattern. Prediction methods that analyze dependencies and correlations between variables to predict future values are also suitable for multivariate time series. Nevertheless, these approaches usually need more time to compute and train, and they might not even be the most appropriate way to go, because the increased complexity of the model used could outweigh any possible improvement in prediction accuracy. In this paper, a thorough comparison study was conducted to analyze the effectiveness of univariate and multivariate predictive analysis on two separate sets of EC data at hourly and daily intervals. To accomplish this, the LSTM algorithm was utilized, which has recently been widely used and recognized as the best-performing algorithm in EC forecasting studies. The comparative analysis is complemented by a comprehensive literature review, meticulously presented in a tabular format, to offer a comprehensive understanding of the univariate and multivariate forecasting methodologies and their respective outcomes. This study stands out due to its incorporation of an extensive literature review to support the experimental research, ensuring a thorough evaluation. Based on experimental studies, univariate forecasting analysis outperformed multivariate forecasting analysis for both hourly and daily interval data sets. Furthermore, the R-squared results of the univariate and multivariate predictive analyses conducted with the hourly data set are significantly higher than those of the same predictive analyses in the daily interval.

Application of Time Series Prediction Models in Chinese Electricity Consumption Analysis

Study focuses on the application of time series prediction models in analyzing electricity consumption in China, aiming to promote the intelligentization and sustainable development of the country's power system. The ARIMA model was chosen for this research, incorporating historical electricity data to forecast future consumption. Through a series of steps including data preprocessing, model construction, and prediction analysis, the results indicate a general growth trend in electricity consumption, influenced by seasonal and cyclical factors. By plotting periodic decomposition charts, the study visually reveals the components and patterns of electricity data, providing scientific decision-making support for the planning, scheduling, and management of the electricity industry.

Assessment of transit bus electricity consumption using a random parameters approach

The shift towards sustainable transportation emphasizes the crucial role of electric transit buses in promoting cleaner and more efficient public transit systems. This study conducts an explanatory analysis of electric bus electricity consumption, with a focus on the impacts of detailed driver behavior and vehicle operation. A random parameters ordered probit model is applied to an electric bus dataset collected from Guangzhou, China. Findings indicate that some factors exhibit consistent impacts on electricity consumption across bus fleets, such as the acceleration rate at bus stops and the number of stops induced by traffic conditions. However, variability exists within the bus fleet regarding the effects of other factors, such as the average speed and handbrake usage. These results underscore the need for targeted strategies, such as maintenance, route optimization, driver training, and policy adjustments in bus operations, to decrease electricity consumption and improve the efficiency of public transit systems.

Geographically weighted regression analysis of electricity consumption in Indonesian households: aligning with SDG 7

Geographically weighted regression analysis of electricity consumption in Indonesian households: aligning with SDG 7

Deep retrofitting toward net zero energy building in the Mediterranean region: A case study of Albania

Like many other Mediterranean countries, Albania faces unique challenges and opportunities to achieve an efficient and fully decarbonized household sector by applying real energy efficiency measures and numbers toward nearly zero-energy buildings. The study findings showed that nZEB in 2030 can be achieved by combining active and passive energy efficiency measures. Behind the study's state-of-the-art stands a multivariable regression analysis of both electricity consumption and electricity generation executed for three validated consecutive years, 2021, 2022, and 2023, including demand side (electricity bills) and supply side generation provided from a nearby existing onsite PV with an installed capacity of 5.5 kWp. A good mathematical correlation is carried out using Durbin – Watson criteria on statistics using RETScreen software to correlate electricity consumption and generation as a function of weather parameters for the tested household and selected region. The goals to meet the nZEB concept require the insulation of the existing exterior wall and window replacements in compliance with the Energy Performance Methodology on Building and installing rooftop Solar Water Heating (SWH) panels of 3.73 m2 as expostulated in scenario 7. The simulation results show that integrating a rooftop PV system with an installed capacity of 5.5 kWp would provide enough electricity to convert residential buildings into Positive Energy Buildings in 2050 based on the selected dwelling and location (Mediterranean region). At the national level, the proposed model would reduce the national annual electricity import level by 1.64 %, saving annually around €31,021,162 and reducing at least 0.068 MtCO2 per year. Subsidizing passive energy efficiency measures at a rate of 3 % per year coupled with onsite renewable energy sources schemes and other incentivization pathways from the government and other interested parties at a rate of 80 % in the roadmap towards nZEB and PEB is mandatory.

Estimation and Sensitivity Analysis of Electric Buses Energy Consumption through Microscopic Simulations on Public Transport Lines

Estimation and Sensitivity Analysis of Electric Buses Energy Consumption through Microscopic Simulations on Public Transport Lines

Municipal Energy Efficiency in Portugal: An Analysis of Electricity and Natural Gas Consumption

Analysing energy consumption efficiency is essential for understanding energy resource consumption patterns, identifying the economic consequences, and developing effective public policies. The study investigates energy consumption efficiency levels in Portuguese municipalities in order to analyse disparities in energy consumption efficiency. The aim is to observe the possible relationship between population density and the energy consumed by residents. The study uses the DEA model to detect benchmarking patterns, inefficiencies, and opportunities for improvement in energy consumption practices. The results suggest that there are serious disparities in energy consumption efficiency between Portuguese municipalities and a significant positive correlation between population density and the amount of energy consumed. The disparities can be attributed to the different levels of adoption of efficient energy consumption practices. The results point to the need to define public policies aimed at promoting more efficient energy consumption practices. The research emphasises the importance of implementing more effective public policies that encourage the adoption of sustainable energy consumption practices by residents. The conclusions have important practical implications for the formulation of public policies aimed at local energy efficiency and the development of sustainable energy consumption.

The analysis of electricity consumption in Kazakhstan

The energy sector is a fundamental element of the economic structure of any state, determining its sustainability, competitiveness and ability to innovate. In this context, Kazakhstan, which has significant reserves of hydrocarbons, energy sources and renewable properties, finds itself at the crossroads of solving issues of energy policy, innovation and sustainable development. The electricity sector is a priority resource necessary to maintain the economic security of the country. This is relevant due to the rapid growth of industrial production and demography in order to maintain increasing demand and increase electricity generation in the country. The purpose of this scientific work is focused on the economic analysis of electricity generation and consumption, as well as the identification of shortcomings and the proposal of recommendations to ensure the sustainable development of the energy sector in Kazakhstan. Using the scientific method of analysis, the study aims to identify trends that determine the level of electricity consumption in the country, as well as to offer recommendations for the sustainable and effective development of this industry. The article is a valuable contribution to strengthening and modernizing the energy system of the country. The results of the research are: the current state of Kazakhstani energy sector is revealed against the background of anti-Russian sanctions, increased energy prices and global uncertainty; a comparative analysis of electricity production is carried out; an economic assessment of primary and final energy consumption from 2015 to 2023 was carried out; the reasons for changes in electricity generation and consumption from the perspective of industrial and consumer demand growth were substantiated; shortcomings were revealed and proposals were made to improve the development and consumption of electricity in the republic. The results obtained have significant potential to initiate certain transformations in the energy sector of Kazakhstan. The article is a scientific work contributing to the development of theory and practice in the field of energy in Kazakhstan. This research contributes to the further development of energy consumption and improvement of the country's energy infrastructure, ensuring its sustainability and competitiveness in the long term.

A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil

The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

Utilizing Machine Learning Techniques for Power Estimation in Residential Electricity Consumption

Electricity consumption analysis and estimation play a vital role in various contexts, ranging from business planning to household financial management. In an era where electrical energy is a primary source of human activity, understanding power usage patterns and estimating them becomes crucial. This research enables an understanding of energy consumption from individual to national scales and supports efficient energy policy planning. Power estimation provides insights into the expenditures that consumers or companies will incur, enabling effective financial planning. Research and development of accurate predictive models using machine learning techniques are essential to providing useful information for the energy industry and the general public. This study uses machine learning to compare the performance of different power prediction models. The decision tree (DT) method, with an MAE of 0.3613, an MSE of 0.4184, and an RMSE of 0.6469, gives the best results. The results show that the model can provide accurate predictions of electricity consumption, potentially becoming a reliable tool in power management and financial management. Its contribution is crucial in the energy industry and daily life, providing insights needed to enhance energy efficiency and budget management. As a result, this research provides relevant and beneficial solutions to energy and financial management for society and industry. Keywords: Electricity consumption analysis, Estimation, Machine learning, Power management, financial management

MATHEMATICAL APPROACH TO FORECAST THE ELECTRICITY CRISES IN PAKISTAN AND ITS MITIGATION

Electricity is widely recognized as the backbone of economic success and progress, playing a vital role in driving socioeconomic development. This comprehensive study conducted an in-depth analysis of Pakistan's electricity consumption and aimed to develop a suitable model for forecasting the country's future electricity consumption (E.C.). Due to the strong positive correlation between E.C. of different sectors with Population and Gross Domestic Product (GDP), multiple linear regressions were employed for the analysis of electricity consumption. To predict population and GDP different mathematical model such as linear, Exponential, Polynomial and logarithmic models were applied on population and GDP data. In order to get the best fitted model a number of goodness-of-fit tests Coefficient of Determination, Sum of Square Errors, Sums of Square Regression, Mean Square Error, Root Mean Square Error, And F- Statistics (Adj-R2, SSE, SSR, MSE, RMSE and F Statistics) applied and got the polynomial model to be the most effective one for population and GDP. According to the goodness-of-fit test-based models, Pakistan's population is on track for steady growth, with projections reaching approximately 244428310 in 2025 and further increasing to 319825879 in 2040. This indicates significant demographic changes on the horizon. At the same time, Pakistan's GDP per capita is expected to rise, starting at 1958.3 $ in 2025 and potentially reaching 3,210.9 $ in 2040, signifying a positive trajectory towards economic development and prosperity in the country. Based on the selected models suggested by goodness of fit tests, the study throws light on the expected future electricity consumption in Pakistan. According to these projections, the expected electricity consumption of Pakistan may reach 121,121.1 GWh in 2025, 138,861.8 GWh in 2030, 157,716.8 GWh in 2035, and 177,686.3 GWh in 2040 respectively.

Analysis and development of criteria for the operational efficiency of urban electric buses

Problem. Currently, manufacturers and research specialists primarily utilize the amount of autonomous mileage and/or the specific electricity consumption of autonomous DEEs per unit of mileage to describe the general characteristics of urban electric buses. However, the structural mass and the key operational parameter, the nominal passenger capacity, are often disregarded. Therefore, comparing electric buses of the same type with identical dimensional parameters based solely on these criteria is illogical and incorrect. Goal. This study aims to establish criteria for the operational efficiency of urban low-floor electric buses of various types, considering autonomous DEE and their charging or replacement systems. These criteria should account for structural mass and nominal passenger capacity, facilitating a proper comparative assessment and the selection of the most optimal options for use in urban passenger transportation systems. Methodology. Criteria for the operational efficiency of city electric buses of various types are proposed based on an analysis of electricity consumption required for both movement and the operation of heating, ventilation, and air conditioning systems. The criteria consider the mass in the equipped state and the main parameter - nominal passenger capacity. Results. Several operational efficiency criteria for urban electric buses, incorporating mass parameters and passenger capacity, are suggested. The feasibility of their application is demonstrated through a comparison of five sketch projects of electric buses of various types with modern models. Originality. The proposed criteria for the operational efficiency of urban electric buses offer a method for adequately comparing any selected models, regardless of their types and technical specifications. Practical value. The proposed criteria for the operational efficiency of urban electric buses of various types can be beneficial for transport company specialists in selecting suitable rolling stock for specific routes. Additionally, design bureau and scientific and technical center specialists can utilize these criteria during the design process of new urban electric transport models.

Analysis of climate-meteorological and technological factors affecting electricity consumption of mining enterprises

Electricity consumption of mining enterprises depends on many factors. The influence of factors on the process of electricity consumption is complex and diverse, and it is impossible to describe it within the framework of deterministic methods, due to the uncertainty of the conditions that determine the influence of factors. These factors include mining geological, mining technological, climate-meteorological, energetic, organizational, operational and others. Energy consumption regimes of mining enterprises depend on many mining technological factors, most of which are insufficiently taken into account in the analysis and planning of electricity consumption.

Time-Stratified Analysis of Electricity Consumption: A Regression and Neural Network Approach in the Context of Turkey

This study aims to apply seasonality and temporal effects in the analysis of electricity consumption in Turkey as a case mixed with regression and neural network methodologies. The study goal is to increase knowledge about the features and trending forces behind electricity usage which provide informed recommendations for smart energy planning and regulation. Comparing and contrasting the regression and neural network models makes it possible to carry out a thorough analysis of the merits and demerits of each model. Moreover, the examination of the limits of the models and their performance in forecasting electricity consumption patterns over the long term is done. The results of this study have a significant impact on power forecasting techniques, and they have meaningful effects on the policymakers, planners and utilities in Turkey. Understanding the story of the use of electricity around the world is very important for the development of sustainable energy policies, resource provision, and the maintenance of reliable and smart energy networks in the country.

Analysis of safe electricity consumption on load side based on attack and defence game model

AbstractWith the improvement of digitalization and intelligence in the power system, the safe operation of the power system is facing enormous challenges. The safe use of electricity on the load side is the key to achieving safe and reliable power system operation. The detection party needs amounts of human and material resources when the power network is attacked. In response to the current difficulties of low detection ability and high detection costs, this paper proposes an attack and defence game model that considers the differences between different nodes, ensuring the safety and economy of electricity consumption while reducing energy waste. At first, the structure of smart meters and the attack characteristics of intruders are summarized, and a basic attack and defence game model is constructed. The Nash equilibrium is then solved, and the optimal strategy for the game between the defender and the intruder is given to balance the relation between detection performance and energy consumption. In response to the differences generated by each node, strategies for attackers to launch attacks on different nodes and the setting of optimal thresholds for other nodes in the defence system are explored. Finally, case studies verify that the proposed model could reduce the cost of intruder detection while ensuring a specific detection rate.

Environmental sustainability in hospitals: Dual analysis of electrical consumption and pollutant emissions

The emission of pollutants following electricity consumption is significant, and hospitals are major contributors to energy consumption. Hence, the aim of this study was a survey of electrical consumption rate and environmental pollutants emissions in selected hospitals. This descriptive-analytical and cross-sectional study was conducted over three years and based on electricity bills and bed indicators in selected hospitals in Tehran, Iran. Emissions Calculator software was used to calculate the emission of pollutants caused by electricity consumption. Kolmogorov Smirnov, Least Significant Difference (LSD), and one-way ANOVA tests were used to analyze the data. Results showed that the total average electricity consumption for each active day bed and occupied day bed was 9.6 and 13.6 times higher than the recommended standards. The average electricity consumption for each active day bed in hospitals A, B, and C was 13.7, 10.2, and 5 times higher than the standard value, respectively, while for occupied day beds, the consumption was 17.3, 15.1, and 8.5 times higher than the standard value, respectively. Additionally, the total electricity consumption in these hospitals resulted in the emission of 41837100 kg of CO2, 124815 kg of SO2, 103699 kg of NOx, 9769 kg of CO, 4885 kg of PM10, and 757 kg of VOCs into the environment. Based on the monthly active bed index and occupied day bed, there was a significant difference in the average electrical consumption among A, B, and C hospitals (p = 0.0001). Electricity consumption in the studied hospitals exceeds standards. Implementing optimal designs for lighting, heating, and cooling, along with energy optimization training, technical audits, and inspections are essential for both economic benefits and reducing environmental pollutants.

Two-stage repeat sintering: A modified method for fabricating hydroxyapatite scaffolds with enhanced mechano-therapeutic properties

A modified two-stage repeat sintering (TSRS) technique has been used to produce hydroxyapatite (HAp) from non-separated animal bones, and the energy consumption per cycle of the proposed TSRS was calculated. The TSRS technique was compared to the conventional one-step sintering method. The energy consumption per cycle of the proposed TSRS was calculated considering economies of scale and typical energy consumption billing in Nigeria. The synthesized powders from both techniques underwent comprehensive characterization through X-ray diffraction (XRD), Fourier transform infrared (FT-IR), and scanning electron microscopy (SEM). The XRD analysis revealed the main reflections of HAp in the (112), (300), and (202) planes. The FT-IR spectra exhibited characteristic bands representative of HAp at all measurement points. Notably, the TSRS-derived sample during the second sintering step showed carbonate bands, suggesting relevance in the bone structure makeup. SEM images unveiled varying crystal morphologies, ranging from irregular shapes in the raw bone (RB) sample to rounded shapes with nucleation in aggregates and agglomerates at micro and macro levels in the TSRS-derived samples. The samples obtained from the TSRS technique demonstrated a high Ca/P ratio that is potentially beneficial for osteoblast adhesion in vivo. The cost analysis of electricity consumption per TSRS cycle, factoring in economies of scale and typical energy consumption billing in Nigeria, amounted to approximately $8 with consideration of the exchange rates at the time of the study. The microhardness measured using the TSRS technique was 0.5 GPa with a 1 MPa compaction pressure, while the samples prepared using the conventional sintering method were too fragile to handle and measure. The obtained microhardness using the modified technique surpassed values reported in various studies. In conclusion, the samples produced using the TSRS technique exhibited promising results regarding energy efficiency, material characteristics, and microhardness compared to the samples produced through conventional sintering. The distinct crystal morphologies observed suggest potential advantages for biomedical applications, particularly in enhancing osteoblast adhesion.

Analisis Decoupling Konsumsi Listrik dan Determinan Emisi Karbon: Studi Kasus Negara ASEAN 2000 – 2018

During 2000 – 2008, electricity took an important role in increasing carbon emissions in ASEAN. In this thesis, the emission’s controlling factors are seen from the energy supply, electricity consumption, GDP, and population in ASEAN. The using method is decoupling analysis which aims to identify the state of each country during the period, Long-term empirical estimation using the fixed effect model (FEM) and short-term estimation with error correction model (ECM). The results show that energy supply has a positive effect on carbon emissions both in the long and short term. Increasing energy supply will have an impact on increasing carbon emissions. Electricity consumption also has a positive effect on carbon emissions in the short term. Decoupling analysis of electricity consumption and national income reveals six decoupling status. Most ASEAN countries have experienced expansive negative decoupling or increase in electricity consumption higher than the increase in national income. Those explanation describe the increasing of national income is still depend on electricity consumption, on the other hand the source of electrical energy mostly comes from fossils in which potentially cause an increasing on carbon emissions.

Performance Comparison of ARIMA, LSTM and SVM Models for Electric Energy Consumption Analysis

The changing needs of electrical energy result in the electrical power needed for everyday life being unstable, so planning and predicting how much electrical load is needed so that the electricity generated is always of good quality. So it is necessary to predict the consumption of electrical energy by using forecasting on the machine learning method. Support Vector Machine (SVM), Autoregressive Integrated Motion Average (ARIMA), and Long Short-Term Memory (LSTM) are models that are often used to overcome patterns in predictions. To find out the best models how to predict electricity consumption in the future and how the SVM, LSTM, and ARIMA algorithms perform in predicting electricity consumption. This research will look for the RMSE value and prediction time, then compare it with the best average value. The results of the study show that the ARIMA model is able to predict electricity usage for the next 1 year period, in the evaluation using the RMSE metric, where SVM shows a much lower value than ARIMA and LSTM. In this case, SVM achieved RMSE of 0.020, while ARIMA and LSTM achieved RMSE of 7.659 and 11.4183, respectively. Even though SVM has a lower RMSE, it is still unable to predict electricity usage for the next 1 year with sufficient accuracy.