Analysis Of Electricity Consumption Research Articles

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.

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.

Analysis of Aerosol Removal Efficiency and Electrical Energy Consumption in the Use of Air Conditioner and Air Purifier in a Room

Analysis of Aerosol Removal Efficiency and Electrical Energy Consumption in the Use of Air Conditioner and Air Purifier in a Room

Electric vehicle energy consumption modelling and analysis: a Malaysia case study

Previously, the Electric vehicle (EV) range anxiety greatly hindered EV adoption. The fear of being stranded due to running out of battery becomes the main concern among EV drivers. This critical issue can be solved by deploying a bigger battery in EVs. However, deploying bigger size battery contributes to the higher price of the EV itself. Hence, the optimal design of the EV powertrain is a critical factor in ensuring EV meets the users’ needs and, simultaneously, can be owned by the potential owners at an affordable price. Accurate assessment of EV energy consumption is crucial in designing an optimal EV powertrain, particularly for developing energy-efficient driving techniques in both manual and autonomous driving. One method is to perform a real-life experiment to determine the EV energy consumption, which is expensive and time-consuming. An alternative to real-world testing is creating an EV energy consumption model with the existing driving cycles. This paper studies the parameters contributing to EV energy consumption based on EV driving cycles available in MATLAB Simulink. The findings of this study will provide insight into the factors that influence EV energy consumption and serve as a guideline in EV powertrain design.

Analysis of Electrical Energy Consumption in Office Buildings of the Institute Technology of Sumatra in Energy Conservation and Efficiency Efforts

Electrical energy is one of the basic human needs in carrying out various activities or daily activities, especially in office buildings such as office buildings of state higher education institutions, namely the Sumatra Institute of Technology (ITERA). Office buildings are the third highest consumer of electrical energy after shopping centers and hotels. The high demand for electrical energy requires the user sector to make efficiency in energy use. Before making efficiency, it is necessary to know the profile of electrical energy consumption including energy consumption intensity (ECI) and significant energy users. Therefore, in this study, an analysis of electrical energy consumption in each ITERA office building was carried out, through observation and interviews according to SNI / ISO 50002: 2018 energy audit procedures. The results obtained show that the total energy use of the 6 largest buildings (A, B, C, D, E, and F) in May 2023 is 144768 kWh and from the calculation of the ECI value is in the very efficient category (7.44 kWh/m2/month). From 6 existing buildings, there are 3 buildings that are very efficient, 1 building is efficient, and 2 buildings are quite efficient. Then, significant energy users were found by 83% in building C AC. Recommendations for saving energy in the effort of energy conservation and efficiency that can be done include, making changes in habits / patterns of use of electrical energy user equipment, especially in air conditioners and dispensers, so as to save electricity 2-21%, and retrofitting refrigerant AC types of split ducts and cassettes that still use R-22 with MC-22 which can save electricity 15-25%.

Long-lasting ceramic composites for surface dielectric barrier discharge plasma actuators

The developed research presents a novel experimental study of the cost-effective MgO-Al2O3, MgO-CaZrO3 perovskite, and thermally stable YSZ ceramic composites for DBD plasma actuators in aerospace applications. This study focuses on the implementation of ceramic DBD plasma actuators for aerodynamic flow control and ice creation mitigation. For this purpose, electrical power consumption analysis, induced flow velocities assessment, and mechanical and thermal characterization were performed. MgO-Al2O3 presented higher induced velocities than its zirconia-based counterparts of up to 3.3 m/s, and lower heat dissipation, achieving a ceiling temperature of 46 ºC, being thereby the best-suited candidate for active flow control mechanisms. In contrast, YSZ had very high-power consumption translated into a maximum surface temperature of 155.4 ºC, establishing itself for ice mitigation. This extensive research evinces that the strategic combination of the developed ceramics' thermomechanical, thermoelectric, and electromechanical properties allows them to be a promising breakthrough material for DBD plasma actuators.

Space-Level air conditioner electricity consumption and occupant behavior analysis on a university campus

In China, despite only 2 % of the population residing on university campuses, these campuses account for 8 % of the total energy consumption. Understanding the space-level energy consumption characteristics and energy-related occupant behaviors on a university campus can help improve campus buildings' energy efficiency. This study investigates 21 office spaces on a university campus to better understand the space-level electricity consumption of air-conditioners (AC) and the occupant behaviors (OB), thermal comfort needs, and window operation. First, Internet of Things (IoT) sensors were installed in the 21 office spaces to collect the indoor temperature and humidity, the electricity consumption of the AC, and the status of doors. The data were collected for the entire cooling season. The space-level occupancy patterns were then analyzed based on the quality of the doors, including the first occupant’s arrival time and the last occupant’s departure time. Moreover, the indoor temperature, humidity, and illuminance were combined to evaluate occupants’ thermal and visual comfort need. In addition, statistical and clustering analyses were conducted on air conditioner operations' duration, frequency, and electricity consumption. The results showed that the spaces were typically occupied from 9:21 to 22:25, with 10 h and 53 min daily. The average indoor temperature was 26.1 °C, ranging from 22.1 °C (5th percentile) to 34.9 °C (95th percentile) when the room was occupied and ranging from 21.7 °C (5th percentile) to 32.5 °C (95th percentile) when the AC was on. The air conditioner's electricity consumption ranges from 11.7 to 152.2 kWh/m2 with a median of 68.9 kWh/m2 and from 46.84 to 1883.7 kWh/person with a median of 464.7 kWh/person. The different occupancy densities often lead to a significant difference in the electricity consumption of air conditioners. The findings of this study can be used to support the development of prototype energy models and occupant-centric control (OCC) models for campus office buildings.

Inactivation efficacy and mechanisms of wavelength-specific UV sources for various strains of Legionella pneumophila serogroup 1

Infections of Legionnaires' disease in the United States caused by Legionella have increased ninefold between the years 2000–2018. Legionella harbored in biofilms or inside amoeba within premise plumbing can be more resistant to disinfectants, thus causing treatment challenges. Ultraviolet-light emitting diodes (UV-LEDs) are an emerging water disinfection technology with several advantages over conventional UV lamps. In this study, we evaluated the effects of UV-LEDs (255, 265, and 285 nm), a low-pressure (LP) mercury UV lamp (254 nm), and a bandpass filtered medium-pressure (MP) mercury UV lamp (220 nm) on properties and inactivation of three strains of L. pneumophila serogroup 1. The UV-LEDs emitting at 255 and 265 nm showed greater inactivation performance against all the strains compared to the UV-LED at 285 nm and the LP UV lamp at 254 nm. Our results showed that strains of the same serogroup exhibited different UV sensitivities. Analyses of DNA and protein damage revealed that UV exposure using 254, 255, and 265 nm predominantly causes DNA damage, while protein damage is predominant at 220 nm. Both DNA and protein damage were observed at 285 nm, but the extent of DNA damage was relatively less significant compared to the other wavelengths. Electric energy consumption analysis showed that water treatment using UV-LEDs is currently unsatisfactory compared to conventional LP UV lamps due to the mediocre wall plug efficiency (WPE) of UV-LEDs. However, recent studies indicate that the WPE of UV-LEDs is continuously improving. Overall, our study highlights that UV-LEDs are a promising technology for inactivating waterborne pathogens and have the potential to replace existing UV mercury lamps for water disinfection applications.

Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions

Demand for battery electric vehicles has grown significantly in recent years as global oil prices have climbed. The current development of battery electric vehicles is still in its infancy, and its energy consumption also requires great attention. Five same model battery electric vehicles' driving condition data of a certain brand in Tianjin area are collected for a whole year, and their energy consumption is analyzed. The results show that the energy consumption of the actual driving conditions in the four seasons is generally higher than the test results under New European Driving Cycle, and only some vehicles' energy consumption in spring and autumn is consistent with the test results under Worldwide Harmonized Light Vehicles Test Cycle. Energy consumption tends to increase at both ends with the change of temperature. The impact of low temperature is greater, because the air conditioner usage rate and power in Winter are higher than those in Summer. Through principal component contribution rate analysis and K-means clustering calculation of micro-trips, the results show that the average energy consumption in urban, suburban and high-speed driving conditions decreases gradually. Driving habits such as rapid acceleration ratio and rapid deceleration ratio also have an impact on energy consumption. The decrease of energy braking recovery indirectly caused by rapid deceleration ratio increase makes the energy consumption per 100 km increase. And the increase of rapid acceleration ratio leads to an increase in energy consumption per 100 km. Finally, the Spearman rank correlation analysis shows that energy consumption has the greatest correlation with the average vehicle speed and air conditioner usage status, followed by the constant speed proportion, idle speed proportion and temperature, and finally the recovery of braking energy per 100 km, acceleration proportion, deceleration proportion and rapid acceleration proportion.

Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0

Accelerating the decarbonisation of the built environment necessitates increasing electrification of end-uses, which in turn poses the issue of rethinking the role of energy efficiency in conjunction with flexibility in grid interaction. This requires a better understanding of the electricity load profiles at hourly or sub-hourly intervals using techniques that are simple, reliable, and interpretable. To this extent, this study proposes a reformulation of the Time Of Week and Temperature modelling approach. This approach is able to separate the energy consumption dependence on building operational characteristics (Time Of Week) and on weather (outdoor air temperature), through a highly automated modelling workflow, necessitating minimal effort for model tuning. These features, along with its intrinsic interpretability due to its formulation using multivariate regression and the availability of open-source software, makes it an ideal starting point for applied research. The case study selected for the research is a fully electrified public building in Southern Italy. The building has been monitored for 5 years, before, during and after the COVID-19 lockdown. The novel model formulation is calibrated using hourly interval data with a Coefficient of Variation of Root Mean Square Error in the range of 20.0–28.5% throughout the various monitoring periods. The counterfactual analysis of electricity consumption indicates a 10.7–26.7% decrease in electricity consumption due to operational adjustments following COVID-19 lockdown, highlighting the impact of behavioural change. Finally, the possibility of additional workflow automation and enhanced interpretability is discussed.

Analysis of Electrical Energy Consumption in a Novel Direct Current Submerged Arc Furnace for Ferrochrome Production

Analysis of Electrical Energy Consumption in a Novel Direct Current Submerged Arc Furnace for Ferrochrome Production

Extreme event counterfactual analysis of electricity consumption in Brazil: Historical impacts and future outlook under climate change

Climate change and other disruptive events have a significant impact on the electrical grid, affecting both power supply and consumption. With the rise in frequency and severity of extreme events, like heatwaves and droughts, the stability and operations of the system are increasingly at risk. The COVID-19 pandemic, unrelated to climate, has also brought about dramatic shifts in global energy patterns. We apply machine learning to model electricity consumption counterfactuals for Brazil, one of the largest hydropower producers, to understand the effects of these events. By training our model on 23 years of data (1999–2021), we achieved a .848 R2 and 2.6% MAPE. This enabled us to assess the impact of historical events on electricity consumption at both hourly and daily levels. Next, we use climate change scenarios to forecast electricity consumption and find that Brazil’s capacity is unlikely to meet demand from 2070 on-wards. Our research provides much needed insight into the impact of extreme events on Brazil, with implications for understanding energy system responsiveness and resiliency. The counterfactual approach proposed is also transferable to other countries and contexts, with the potential for new application areas given interactions between extreme events, climate change, and transitioning energy systems.

Charging station forecasting and scenario analysis in China

China's electric vehicle industry has gained momentum due to multiple factors, but there is still a gap in demand for charging stations. China's subsidy policy for EVs is gradually withdrawing from the market and increasing subsidies for charging facilities to stabilize the growth of EVs. Taking the number of EVs as the decision parameter, this paper proposes a multi-factor combination prediction model of grey correlation and long and short-term memory. The model predicts that the number of EVs in China will reach 1.02 billion by 2030. The importance of factors is discussed qualitatively through scenario setting analysis of EV electricity consumption and power supply. It is found that charging stations in China will show varying degrees of growth from 2021 to 2030. Finally, corresponding suggestions are put forward for charging stations, including charging subsidies, technology R&D innovation policies, and licensing quotas.

Analysis of Electricity and Water Consumption in Existing Mosque Buildings in the UAE

According to the World Economic Forum, the building sector is responsible for 40% of global energy consumption and 33% of greenhouse gas (GHG) emissions, and this is expected to increase due to population growth and the subsequent impact on the environment, economy and health. To tackle the problem, countries have set new construction codes, policies and regulations for the construction of new buildings in an effort to make them greener. However, there is a need to enhance the status of the existing buildings, especially mosques, as they are the main contributors to energy usage and water consumption in the United Arab Emirates (UAE). Therefore, this research seeks to fill this gap, aiming to evaluate the energy usage and water consumption practices employed in the existing mosque buildings within the UAE and to provide recommendations for improving the sustainability of mosques, with a focus on the environmental and economic pillars. The methodology relies mainly on data collected from 146 existing mosque buildings that have undergone energy saving audits across the UAE. Descriptive statistical analysis is performed to analyze the data from the period of 2018–2019 in order to determine the most significant factors related to energy inefficiency in existing mosque buildings in the UAE and to determine the most cost-effective and energy-saving corrective measures for energy and water conservation. The findings further enhance the standard of experience for mosque visitors (social aspect); reduce energy bill expenses, providing an acceptable return on investment from the proposed energy conservation measures for stakeholders (economic); and reduce the overall energy consumption, which can reduce the total CO2 emissions from mosque buildings (environmental).

A detailed analysis of electricity consumption at the University of Castilla-La Mancha (Spain)

The current energy crisis has drastically altered forecast electricity plans and budgets for European university campuses. This situation heightens the need to analyze their electrical consumption, with two main goals: identifying their patterns and promoting the development of renewable installations for these consumers. Previous research has focused only on aggregated demand data, with the studies being based on estimations and forecasts, and focused mainly on single buildings. Moreover, there is a lack of scientific papers that provide a replicable codebase for electricity analysis. Our work presents a novel methodology to overcome these research gaps, proposing the first comprehensive, replicable and scalable codebase to analyze electricity consumption in universities. It is based on three steps. The first comprises automated data collection of real electricity measurements at each electricity supply point. The second develops the complete analysis of electricity consumption. The last step parameterizes this consumption by identifying seasonal and daily profiles. The research was applied to the University of Castilla-La Mancha, campus Albacete (Spain) case study. The results revealed the 4 highest electricity-demanding buildings: Biomedical Complex, Higher Technical School of Industrial and Computer Engineering, Vice-rectorate and Library, and Higher Technical School of Agricultural and Forestry Engineering. The results are thus of great value for other educational buildings.