Factors Influencing Energy Consumption Research Articles

Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm.

Dynamic Simulation Analysis of Influencing Factors of Energy Consumption of Public Building Envelope in Central China

It difficult for buildings in cold regions to reasonably meet ultra-low energy consumption requirements. To explore the influencing factors of building energy consumption in this type of climatic region, this paper takes a typical public building in Zhengzhou, China as a case study to dynamically simulate an entire building. The influence of the window glass, window-to-wall ratio (WWR), thermal insulation material thickness and other factors on building energy consumption is considered. The sensitivity of envelope design factors to energy consumption is analyzed by the orthogonal test. The results indicate that the effect of changing the technical parameters of the external envelope structure on building energy efficiency is significant. Moreover, range analysis is used to determine the primary and secondary factors with regard to their influence on the energy consumption of the building. The WWR, the type of external window glass and the thickness of the external wall insulation layer should be considered first in energy-saving design; the energy-saving potential of the roof insulation thickness is not as significant as these factors. The results of the study can provide a reference for the energy-saving design of public buildings in Central China.

Identifying salient features of cooling energy usage of commercial buildings using explainable artificial intelligence

ABSTRACT Electrical energy usage of commercial buildings (over 60% in the USA) is of growing concern due to its impact on the environment. To reduce it, it is necessary to identify the factors contributing to commercial building energy usage. Towards this, we studied the importance of various building features and their contributions on cooling energy, using two datasets of Commercial Building Energy Consumption Survey (CBECS) 2018 and 2012. For this, Shapash, an explainable artificial intelligence technique, was used, with Random Forest, to predict the cooling energy usage intensity (EUI). The two most important features for 2018 are cooling degree days and principal building activity, while cooling degree days and cooling percentage were for 2012 with 54.79% and 33.46% contributions of the total consumption respectively. The comparison of feature importance indicated that cooling degree days are the topmost important feature impacting cooling EUI and have a more significant contribution of 34.29% for 2018 and 19.68% for 2012. Overall, the most important features impacting cooling EUI were cooling degree days, principle building activity, cooling percentage and total hours open per week. The results from this study provide insights into the most significant factors influencing energy consumption and help in developing strategies for reducing it.

Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification.

Optimization and Economic Analysis of Green Public Building Envelope Structure in Chongqing

Public buildings such as colleges and universities in Chengdu can be studied from the aspects of the main factors influencing buildings’ energy consumption. This study analyzed the indoor thermal environment of the buildings and characteristics of their palisade structures, with their thermal performance and energy consumption, as main influencing factors of energy consumption. In the present study, these public buildings have been considered as the research object, their data has been measured through field investigation and theoretical analysis, numerically simulated- based on the actual features- and compared by computer simulation to describe the influence of thermal performance of different envelope structures on energy consumption. Also, the relationship between different thermal performance indexes and energy consumption is established. The advantage of this study is mainly to our green and energy-saving society. “Building a conservation-oriented and environmentally friendly society” is a basic state policy of China. Due to their high energy consumption ratio and greater difficulty in energy conservation, public buildings are the key places in emission reduction. The selection of appropriate green building technology is the key to sustainable development of public buildings.

Factors Influencing Energy Consumption from China’s Tourist Attractions: A Structural Decomposition Analysis with LMDI and K-Means Clustering

Factors Influencing Energy Consumption from China’s Tourist Attractions: A Structural Decomposition Analysis with LMDI and K-Means Clustering

Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms

The share of residential building energy consumption in global energy consumption has rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption under different indoor and outdoor conditions is an essential step towards improving energy efficiency and reducing carbon footprints in the residential building sector. In this paper, a PSO-optimized random forest classification algorithm is proposed to identify the most important factors contributing to residential heating energy consumption. A self-organizing map (SOM) approach is applied for feature dimensionality reduction, and an ensemble classification model based on the stacking method is trained on the dimensionality-reduced data. The results show that the stacking model outperforms the other models with an accuracy of 95.4% in energy consumption prediction. Finally, a causal inference method is introduced in addition to Shapley Additive Explanation (SHAP) to explore and analyze the factors influencing energy consumption. A clear causal relationship between water pipe temperature changes, air temperature, and building energy consumption is found, compensating for the neglect of temperature in the SHAP analysis. The findings of this research can help residential building owners/managers make more informed decisions around the selection of efficient heating management systems to save on energy bills.

A multi-dimension coupling model for energy-efficiency of a machining process

Energy-efficient machining has become imperative for energy conservation of manufacturing sectors. The energy characteristics of machining process tend to be very complex, varying substantially with respect to different configurations of machine tool, workpiece and process parameters. This paper undertakes this challenge and explores the energy consumption characteristics of machining process adaptive to different machine tools, workpieces and process parameters. A multi-dimension coupling model of energy consumption for machining process is first established by considering specifications of machine tools, workpieces and processes. Then the influence factors of energy consumption are systematically analyzed from a multi-dimensional perspective. The internal interact relationship among each dimensional parameter is illustrated. To validate the effectiveness of the proposed energy model and determine the energy-efficient machining configurations with related to machine tools, workpieces and process parameters, a series of experiments are carried out on a CNC vertical machining center. Experimental results show that the optimal machining configurations can effectively reduce energy consumption and simultaneously improve energy-efficiency of CNC machining.

Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption

The Internet of Things (IoT) is currently reflected in the increase in the number of connected objects, that is, devices with their own identity and computing and communication capacities. IoT is recognized as one of the most critical areas for future technologies, gaining worldwide attention. It applies to many areas, where it has achieved success, such as healthcare, where a patient is monitored using nodes and lightweight sensors. However, the powerful functions of IoT in the medical field are based on communication, analysis, processing, and management of data autonomously without any manual intervention, which presents many difficulties, such as energy consumption. However, these issues significantly slow down the development and rapid deployment of this technology. The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading. The distance between nodes is one of the factors influencing energy consumption. In this article, we have proposed direct communication between nodes to avoid collision domains, which will help reduce data retransmission. The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.

Irrigation scheduling of pressurized irrigation networks for minimizing energy consumption

AbstractAlthough pressurized irrigation systems can increase water use efficiency, the energy requirements of these irrigation systems are much higher than those of gravity‐fed systems in water transport and distribution. To reduce irrigation energy consumption, this research introduces two factors influencing energy consumption: branch pipe assigned queue and branch pipe assigned times. The traditional single water distribution and sequential queue are optimized as multiple water distributions and stochastic queues. Combined with traditional motor frequency optimization, eight scheduling scenarios are established based on these three optimization factors and compared with the original scenario. Next, this study optimizes the discharge of all hydrants for each scenario using a genetic algorithm with the same framework and operational process. Predictions and experiments in a case study in eastern China indicated that only optimizing the branch pipe assigned queue or times cannot significantly reduce energy consumption, while an energy‐savings rate of 8% can be achieved by simultaneously optimizing the branch pipe assigned queue and times. In addition, the energy savings rate of only optimizing the motor operating frequency is 11%. However, an energy‐saving rate of 19% can be obtained by combining branch pipe‐assigned times optimization with motor frequency optimization.

Analysis of the Electricity Consumption in Municipal Wastewater Treatment Plants in Northeast China in Terms of Wastewater Characteristics.

A municipal wastewater treatment plant plays an important role in treating urban sewage and reducing the quantity of pollutants discharged into rivers. However, the energy consumption of the municipal wastewater treatment industry is large. High energy consumption indirectly produces ecological damage, accelerates the energy crisis, and increases carbon emissions. For energy conservation and emission reduction in wastewater treatment plants, it is first necessary to identify the main factors influencing energy consumption. Electricity consumption accounts for more than 80% of the energy consumption of wastewater treatment plants. Wastewater quantity and wastewater quality have become the key influencing factors of energy conservation and consumption reduction in wastewater treatment plants. In this study, a municipal wastewater treatment plant in Northeast China was selected as the research object, and the measured data, such as air temperature, wastewater quantity, wastewater quality, and electricity consumption of the plant from 2017 to 2020 were statistically analyzed to explore the influences of temperature and wastewater quantity and wastewater quality indicators of influent and effluent on energy consumption. Firstly, the range of influent quantity in the wastewater treatment plant was large. The influent quantity in summer was high because some rainwater entered the sewage treatment plant. In winter, average daily electricity consumption (ADEC) was higher than that in summer. The relationship between ADEC and the wastewater quantity showed a positive correlation, and ADEC slowly increased with the increase in wastewater quantity. Electricity consumption per unit of wastewater (UEC) was negatively correlated with the wastewater quantity, but the correction coefficient in winter was larger than that in summer. Secondly, the ranges of chemical oxygen demand (CODCr) and ammonia nitrogen in influent were large, and the ranges of CODCr and ammonia nitrogen in effluent were small. Influent CODCr concentration was negatively correlated with influent ammonia nitrogen concentration. ADEC increased slightly with the increase in influent CODCr concentration. In winter, the increasing trend of ADEC with the influent CODCr concentration was higher than that in the summer. The increasing trend of UEC with the increase in influent COD concentration in summer was more significant than that in winter. Thirdly, influent CODCr in 11.6% of the samples exceeded the corresponding designed value, and influent ammonia nitrogen concentration in 41.4% of the samples exceeded the corresponding designed value. Effluent CODCr in 10.6% of the samples exceeded the First Level Class B standard in "Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants (GB18918-2002)", and unqualified CODCr in 94% of the effluent samples was ascribed to the unqualified ammonia nitrogen concentration in the influent samples. The electricity consumption level under abnormal conditions was higher than that under normal conditions. Fourthly, ADEC was positively correlated with the average daily CODCr reduction. The correction coefficient of ADEC with average daily CODCr reduction was greater in winter than that in summer. Fifthly, the average electricity consumption per unit of wastewater was close to the national average energy consumption, displaying the characteristics of high energy consumption in winter and low energy consumption in summer. The correlation analysis results of unit electricity consumption and temperature showed that when it was below 0 °C, the lower the temperature, the higher the electricity consumption. In Northeast China, the influences of seasons and temperatures on the electricity consumption of sewage plants were obvious. Accordingly, it is necessary to implement the diversion of rainwater and sewage, reduce the discharge of unqualified wastewater from enterprises, and take thermal insulation measures in winter. In addition, activated sludge microorganisms suitable for a low temperature area and the optimal scheduling of sewage pipe networks can also improve the operation and management of sewage treatment plants.

Analysing the real-world fuel and energy consumption of conventional and electric cars in Europe

The transport sector constitutes one of the main sources of greenhouse gas emissions in the European Union. Although the literature is rich in studies assessing the factors influencing energy consumption in real world driving, few analyse the divergence between certified and real-world consumption on a fleet level and annual temporal resolution. The present work builds on the findings of previous studies to provide a complete simulation-based approach for fleet-level projections in real-world scenarios. A tailored simulation framework for fleet-wide analysis has been updated and validated extensively for current vehicle configurations. In addition to modelling, the analysis used data describing real-world conditions and vehicle characteristics representative of the European passenger car fleet and operation profile. Validation demonstrates good accuracy in simulating the vehicle measured consumption values with a near-zero mean error when simulating laboratory and on-road measurements. The impact of the certification procedure was quantified at 6% for both the combustion engine and battery electric vehicles. The influence of real-world factors such as traffic, ambient temperature and cabin air conditioning was quantified, and the results show a greater energy impact in the cold than in warm conditions. Different operating conditions were simulated to investigate the model’s capacity to estimate the distribution of the average sales-weighted gap in energy consumption between officially reported CO2 values and the real-world ones. The study focuses on three scenarios: average European conditions, the best and worst case for assessing the extremes, and a Monte Carlo-based scenario to determine the impact of warm and cold periods. Results show average gap values of 13.5% (best case) and 34.5% (worst case) for conventional vehicles and −4.5% (best case) and 23.9% (worst case) for battery electric ones. Warm and cold weather conditions result in 5.5 and 7.5% increases for conventional and hybrids and 7.5 and 15% for electrics, respectively.

Modeling of energy consumption factors for an industrial cement vertical roller mill by SHAP-XGBoost: a "conscious lab" approach

Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.

Evaluation method of energy efficiency index of smart energy station based on subjective weight and entropy evaluation method

Smart energy station plays the pivotal role of power grid in energy collection, transmission, conversion and utilization due to its characteristics of multi-station integration. In order to accurately evaluate the energy efficiency level of smart energy stations and achieve economic and efficient electricity consumption, an energy efficiency evaluation system suitable for the form of three stations in one is proposed. Firstly, the influencing factors of energy consumption of each station are analyzed, the index system of smart energy stations is established, and the calculation method of each index is given. Secondly, based on the index system of energy efficiency, through the combination of the subjective weighting method and entropy evaluation method to determine the index weight, combined with the score and weight of energy efficiency for smart energy efficiency assessment, based on three different running environment of wisdom energy station simulation comparison, get the efficiency score, verify the feasibility of the assessment method, and gives specific suggestions on saving energy consumption.

Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods

Low-income households face long-standing challenges of energy insecurity and inequality (EII). During extreme events (e.g., disasters and pandemics) these challenges are especially severe for vulnerable populations reliant on energy for health, education, and well-being. However, many EII studies rarely incorporate the micro- and macro-perspectives of resilience and reliability of energy and internet infrastructure and social-psychological factors. To remedy this gap, we first address the impacts of extreme events on EII among vulnerable populations. Second, we evaluate the driving factors of EII and how they change during disasters. Third, we situate these inequalities within broader energy systems and pinpoint the importance of equitable infrastructure systems by examining infrastructure reliability and resilience and the role of renewable technologies. Then, we consider the factors influencing energy consumption, such as energy practices, socio-psychological factors, and internet access. Finally, we propose interdisciplinary research methods to study these issues during extreme events and provide recommendations.

Determinants of Electrical and Thermal Energy Consumption in Hospitals According to Climate Zones in Poland

Energy use in hospitals is higher than in other public buildings, so improving energy efficiency in healthcare buildings is a significant challenge in this sector of engineering. For this, it is necessary to know the various determinants of energy consumption. Until now, the main factor affecting energy consumption in healthcare facilities studied in the literature was hospital capacity. However, the commonly used variables connected with hospital size and the number of beds do not take into account the medical activities carried out in these buildings. Assuming that energy consumption in hospitals is multiple and shaped by many factors that overlap, not only on an individual level but also on a higher scale level, this study devises a more integrated approach to its determinants. This study aims to investigate the determinants of electrical energy costs (EEC) and thermal energy costs (TEC) in Polish hospitals with regard to factors related to their size, work intensity and climate zones. The analysis was carried out using financial and resource data from all Polish hospitals for the years 2010–2019. The study used a multivariate backward stepwise regression analysis. In order to use climate as a moderating variable, a sample of Polish hospitals from 16 Polish NUTS 2 was divided into four climate zones. This article provides new empirical evidence on the determinants of electricity consumption in Polish hospitals related to their size and medical activity, taking into account climate zone as a moderating variable. The results of the analysis show that both electricity and heat consumption in hospitals are positively related to the number of doctors, beds and the number of medical operations performed. As expected, larger hospitals seem to use more energy. Moreover, there is regional heterogeneity in energy consumption in hospitals related to the climatic zone in which they operate. The conducted analysis shows that Polish hospitals located in the warmest climatic zone are characterized by higher energy consumption than hospitals in the coldest zone. It especially regards EEC in surgery hospitals. The warmer the climate zones, the higher intensity in terms of the number of surgeries, the higher EEC. In terms of nonsurgical hospitals, the influence of climate zone on EEC was not observed. Knowing the factors influencing energy consumption in hospitals can facilitate the correct adoption of an energy-saving strategy in the health sector, which is a reasonable response to climate change and supports a healthy and sustainable future.

Drag reduction mechanisms on a generic square-back vehicle using an optimised yaw-insensitive base cavity

Regulations on global greenhouse gas emission are driving the development of more energy-efficient passenger vehicles. One of the key factors influencing energy consumption is the aerodynamic drag where a large portion of the drag is associated with the base wake. Environmental conditions such as wind can increase the drag associated with the separated base flow. This paper investigates an optimised yaw-insensitive base cavity on a square-back vehicle in steady crosswind. The test object is a simplified model scale bluff body, the Windsor geometry, with wheels. The model is tested experimentally with a straight cavity and a tapered cavity. The taper angles have been optimised numerically to improve the robustness to side wind in relation to drag. Base pressures and tomographic Particle Image Velocimetry of the full wake were measured in the wind tunnel. The results indicate that a cavity decreases the crossflow within the wake, increasing base pressure, therefore lowering drag. The additional optimised cavity tapering further reduces crossflow and results in a smaller wake with less losses. The overall wake unsteadiness is reduced by the cavity by minimising mixing in the shear layers as well as dampening wake motion. However, the coherent wake motions, indicative of a balanced wake, are increased by the investigated cavities.Graphical abstract

Effective Permutation Encoding for Evolutionary Optimization of the Electric Vehicle Routing Problem

This paper addresses the problem of route planning for a fleet of electric vehicles departing from a depot and supplying customers with certain goods. This paper aims to present a permutation-based method of vehicle route coding adapted to the specificity of electric drive. The developed method integrated with an evolutionary algorithm allows for rapid generation of routes for multiple vehicles taking into account the necessity of supplying energy in available charging stations. The minimization of the route distance travelled by all vehicles was taken as a criterion. The performed testing indicated satisfactory computation speed. A real region with four charging stations and 33 customers was analysed. Different scenarios of demand were analysed, and factors affecting the results of the proposed calculation method were indicated. The limitations of the method were pointed out, mainly caused by assumptions that simplify the problem. In the future, it is planned for research and method development to include the lapse of time and for the set of factors influencing energy consumption by a moving vehicle to be extended.

USING THE UNIVERSITY ENERGY EFFICIENCY KNOWLEDGE HUB FOR ENERGY CERTIFICATION AND ENERGY AUDITS OF HIGHER EDUCATION INSTITUTIONS

BACKGROUND AND OBJECTIVES. The general problem of the research is to define the essence of university energy efficiency as a special type of management of higher educational institution activity, increase of its energy autonomy level, scientific research on economical use of energy resources. University energy efficiency management is a special type of management, which is based on finding new opportunities to save energy resources based on innovations, ability to attract resources from a variety of sources.METHODS. Logic, system and statistical analysis, and multiple regression methods were used to conduct energy monitoring of HUB use of energy efficiency knowledge. The method of cluster analysis was used for energy audit and energy certification of university buildings. The average value, value of mode and median, indicators of variation (variation range, average linear deviation and variation coefficient) of daily electricity consumption of building No.4 of Kyiv National University of Technologies and Design were calculated, statistical analysis of obtained data was made.FINDINGS. Existing automatic energy accounting systems in university buildings were reviewed, a comparative table was compiled and the systems were ranked according to the sum of the scores obtained.The comparative analysis is carried out according to a set of criteria, including the unique features that distinguish these systems from each other. Based on the ranking results, the best model is selected and its advantages and disadvantages are identified. A simplified list of requirements and necessary functionality for the use of energy efficiency knowledge HUB for energy monitoring, energy audits and energy certification of university buildings has been developed.CONCLUSION. The obtained asymmetry coefficient made it possible to conclude that there is a right-hand asymmetry in the amount of energy used in the university. The selected main factors influencing energy consumption allowed to monitor the energy efficiency of the university in 2020. The use of multiple regression equation allowed to take into account the main factors of energy consumption, the extent of their influence, to compare the obtained results with the actual consumption, to build energy profiles and to carry out energy certification of all buildings of Kyiv National University of Technologies and Design.

Analysis of Energy Consumption Level and Influencing Factors of Highway Service Area

Highway service area has the characteristics of large passenger flow, complex system equipment, diversified operation management, centralized energy use and complex influence mechanism, which results in the difference of energy consumption between service area and conventional public buildings, but the existing facilities and operation management mode of service area have not reached the optimal state, so it still has high energy-saving potential. Based on the analysis of the principal component analysis, the influencing factors of energy consumption of highway service area are analyzed, the main influencing factors of energy consumption of highway service area are selected, and the close relationship between the annual total energy consumption of highway service area and the influencing factors such as building scale, traffic flow and human flow is determined. Finally, by means of multiple regression analysis, the low-impact factors are further eliminated, and the functional relationship between the total energy consumption and the influencing factors is determined and verified, which is helpful to provide the basis for the service area to adopt the corresponding energy-saving measures.