Factors Affecting Energy Consumption Research Articles

Анализ влияния климатических факторов на потребление электроэнергии предприятиями в неперевозочных видах деятельности ОАО «РЖД»

Purpose: analysis of the costs of energy consumption by enterprises of JSC Russian Railways by type of fuel, identification of the most costly type of energy. Systematization of factors influencing energy consumption. Characteristics of climatic factors affecting energy consumption. The task was set to assess the possibility of using ASKUE data to improve the system for analyzing and planning energy consumption in non-transportation activities of JSC Russian Railways. Analysis of climatic factors affecting electricity consumption by enterprises in non-transportation activities of JSC Russian Railways. Determine the maximum time interval for analyzing energy consumption data. Methods: collecting information on electricity consumption and changes in climate characteristics. The main research method is the method of correlation analysis, which allows us to identify the most important factors influencing electricity consumption. Based on the correlation coefficient, a correlation matrix will be compiled using the Statistica software package. Results: climatic factors have been identified that have the greatest impact on the energy consumption of the enterprise in non-transportation activities of JSC Russian Railways. The maximum enlarged interval for analyzing energy consumption data has been clarified. Practical significance: climatic factors have been proposed, the characteristics of which must be relied upon when planning electricity consumption at non-transportation enterprises of JSC Russian Railways. These factors can be used in the further development of a model for planning energy consumption. The maximum enlarged interval for analyzing influencing factors has also been identified, allowing to obtain accurate results. The possibility of using hourly ASCAE data to assess and identify climatic factors affecting electricity consumption by non-transportation enterprises has been proven.

Circuit categorization approach of office building energy consumption based on data features for energy-saving diagnosis

Energy-saving in office buildings is crucial. Research on data-driven diagnostics of building energy consumption is pivotal. The emergence and development of itemized electricity platforms have expanded the scope of diagnosis from total building energy consumption to individual electrical equipment. However current studies rarely address the characteristics of sub-circuits. This paper highlights the importance of recognizing the features of electrical equipment sub-circuits based on historical energy consumption data by examining various aspects of energy use in office buildings, including electrical consumption disaggregation, equipment operational strategies, sub-item consumption proportion, and sub-circuit composition. A circuit categorization approach in office buildings by features analysis of historical data is innovatively proposed. Operational conditions, time series stationarity, and correlations with factors affecting energy consumption are considered in historical data feature analysis. The method categorizes the electric circuits into 5 categories with typical features based on the ADF-KPSS co-test and Spearman correlation analysis. The rationality and validity of the circuit categorization method were verified with actual building data in case studies. On this basis, an interpretable energy consumption anomaly diagnosis method based on the categorization results is proposed. The circuit categorization approach explores how to further investigate the typical features of sub-circuits, and provides new directions for subsequent building operation and maintenance(O&M) research, including energy consumption diagnostics. Additionally, it offers decision support for building managers for O&M evaluation from an interpretable perspective.

Based on the improved fuzzy analytic hierarchy and the TSE-MLR model energy consumption prediction of university: A case study

The accurate prediction of building energy consumption on university campuses is a significant research area. Current studies often focus on predicting the energy consumption of specific building areas or individual equipment, and typically consider only one factor, limiting the accuracy and applicability of the predictions. This study introduces the Time Segmented Energy-Multiple Linear Regression (TSE-MLR) prediction model, which integrates the improved fuzzy analytic hierarchy and the multiple linear regression algorithm. The model is compared with traditional (MLR, BP) and advanced (RNN) models, and their various indexes are discussed and analyzed. By collecting meteorological and energy consumption data from the study site over the past 12 years, the key factors affecting energy consumption on the university campus were identified using the improved fuzzy analytic hierarchy. Subsequently, the TSE-MLR model was trained using energy consumption data from 2010 to 2016 and validated using data from 2017 to 2019. The prediction results of the TSE-MLR model were compared with those obtained through Multiple linear regression, BP neural networks, and RNN. The results demonstrated that the TSE-MLR model significantly reduced the prediction error by 13.8 % and exhibited higher accuracy compared to the other models. Therefore, the TSE-MLR model introduced in this study offers a new and effective approach to predicting university energy consumption and supporting energy management using existing data from university building operations across different periods.

Energy consumption decomposition analysis using European official statistics: Methodology and input data

Deciphering energy efficiency is a critical component for the sustainability and energy policies of the European Union (EU) and its Member States. Decomposition analysis is a key method that helps distinguish real energy efficiency gains from other external factors. This article presents a decomposition analysis method using official EU statistics, its results, and associated limitations. Although separating structural changes and activity levels from energy efficiency poses a challenge, an adapted Logarithmic Mean Divisia Index (LMDI) method was utilised to isolate and highlight energy efficiency. This article outlines this method and how the European official statistics were exploited. Firstly, it examines the factors affecting energy consumption in various sectors within EU-27. Secondly, it examines the factors affecting energy consumption in various sectors. In doing so, it also addresses potential obstacles in data collection, and presents improvements to the LMDI analysis. The findings in this study make a substantial contribution to the fields of national statistics, methodological applications, and energy data analysis in the context of the EU’s energy policies.

Studying regional low-carbon development: A case study of Sichuan Province in China.

The unavoidable option for socially sustainable development is a low-carbon economy. One of the essential steps for China to attain high-quality development is reducing carbon emissions. It is necessary to realize low-carbon development in Sichuan, as it is not only an important economic zone but also an ecological protected area. The concurrent relationship among energy consumption, carbon emissions, and economic growth was examined in this study using the Tapio decoupling indicator, and the factors affecting energy consumption and carbon emissions in Sichuan were broken down using the logarithmic mean Divisia indicator (LMDI). The findings demonstrate a fundamental relative decoupling relationship between Sichuan's energy use and carbon emissions. Analysis of energy consumption and carbon emissions in Sichuan Province from 2005 to 2020 shows distinct patterns. From 2005 to 2012, in 2014, and from 2016 to 2020, the relationship between energy use and carbon emissions was relatively decoupled, with decoupling values ranging between 0 and 1. Absolute decoupling occurred in specific years: 2010, from 2013 to 2018, and in 2020. These periods are characterized by economic growth alongside reductions in carbon emissions. Factors affecting energy consumption and carbon emissions were consistently analyzed, showing similar impacts throughout the study periods. We find that population and economic growth are the main driving forces of these effects. The effects of energy intensity and industrial structure mainly play restraining roles, and the latter has a slightly weaker effect than the former.

Factors Influencing Public Building Energy Consumption: A Case Study of Changjiang River Administration of Navigation Affairs in China

Based on the energy consumption data of the Changjiang River Administration of Navigation Affairs public building located in Wuhan, China, this paper analyzes the energy consumption characteristics of public buildings. Using the DesignBuilder energy simulation software (v7.0.0.096), various factors influencing building energy consumption are investigated. Furthermore, SPSS (v26.0.0.0) is employed to conduct correlation analyses between measured and simulated energy consumption data. The results indicate that the most significant factors affecting energy consumption are outdoor temperature, office equipment usage time, anticipated room temperature settings, boiler usage time, and per capita daily water consumption. By exploring the factors influencing energy consumption in public buildings, this study contributes novel insights into setting targets, adjusting schemes, and achieving energy-saving effects.

Analyzing Energy Consumption in Universities: A Literature Review

This study investigates energy consumption in general and in universities in particular. A literature review was used as the methodology of the study to show the energy consumption purposes, major findings, research gaps, limitations, and future research about how to analyze energy consumption levels. The number of references examined was 60, with 29 references about universities and some of them are concentrating on the buildings’ consumption in the GCC area. Most of the references concentrated on investigating case studies in different countries. Analysis of the findings showed that tools such as big data analytics, machine learning, simulations, and predictive models were used. However, big data analytics was the most important one. It was found that AC systems are the most important component of energy consumption, especially in hot weather countries. The studies also showed that monitoring the consumption rates will reduce the total consumption of energy. In universities, the type of the building and the day index were found the most important factors affecting energy consumption. Factors such as number of smart devices, location, and floor space were found to have different positive and negative impacts on energy consumption from one study to another. Recommendations for future research were also presented.

A soft ground micro TBM’s specific energy prediction using an eXplainable neural network through Shapley additive explanation and Optuna

In tunnel construction, efficiently predicting the energy usage of tunnel boring machines (TBMs) is critical for optimizing operations and reducing costs. This research proposes a novel method for predicting the specific energy of micro slurry tunnel boring machines (MSTBMs) using an explainable neural network (xNN) that leverages operator-monitored data. The xNN model provides transparency and interpretability by integrating the Shapley additive explanation (SHAP) technique, enabling tunneling engineers and operators to gain valuable insights into the prediction process. Extensive data from MSTBM umbrella pipe support excavation are the foundation for training, testing, and unseen data in the xNN model. The specific energy formula derived from the operational parameters of the MSTBM defines the dependent variable for the xNN model. The test dataset evaluates the model’s performance with an R² of 98.7%, an MSE of 2.40, and an MAE of 0.003, demonstrating its accuracy and reliability. Ten percent of the dataset was reserved as unseen data to assess the model’s generalization capabilities. Upon evaluation, the model achieved an R2 value of 89%, an MAE of 0.01, and a root mean squared error (RMSE) of 0.01. The xNN empowers operators to optimize operational parameters and promote more efficient and sustainable tunneling practices by identifying influential factors affecting energy consumption through its interpretable nature. This research has significant implications for the future of underground construction, paving the way for improved resource management.

Benzoic acid catalyzed production of xylose and xylooligosaccharides from poplar

The proficient synthesis of xylose and xylooligosaccharides (XOS) from lignocellulosic biomass proved to be a formidable undertaking, particularly in the absence of preliminary hemicellulose isolation. In this study, the potential of benzoic acid (BZA), a non-toxic and edible acidic catalyst, was assessed for the pretreatment of poplar in order to produce xylose and xylooligosaccharides (XOS). At low temperatures (< 160°C) or low acid concentrations (< 2%), the main product obtained from the depolymerization of hemicellulose was XOS, and no significant depolymerization occurred on a large scale. The results showed that the highest extraction yield of hemicellulose was 93.1% with 2% BZA at 160°C for 0.75 h, hemicellulose was mainly converted into xylose and XOS, with xylose accounting for 27.1% and XOS accounting for 46.7%. Meanwhile, the results of the process simulation of industrial energy consumption indicated that the amount of water added was the main factor affecting energy consumption in industrialization, followed by temperature. This study presented a promising approach for the industrial-scale manufacturing of xylose and XOS from poplar as a primary resource.

Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

ABSTRACT With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models.

Energy Consumption in Higher Education Institutions: A Bibliometric Analysis Focused on Scientific Trends

While universities are expected to exemplify sustainable practices, they often encounter high energy demands. This dichotomy highlights the necessity for research into their energy consumption. Through a Systematic Literature Review, we examined international research trends in this field and explored factors influencing energy consumption. The importance of this article stems from its novel approach to energy consumption in universities, addressed from a global and comprehensive perspective, offering generalizable insights. Additionally, it pioneers in the use of a market concentration indicator (Herfindahl–Hirschman index) to measure the level of diversity in various bibliometric aspects. The extended perspective of our approach helps to close knowledge gaps about scientific trends and common energy consumption factors. Our results show that this topic has been investigated with limited involvement of social sciences. Building function, research intensity, and disciplinary orientation are distinctive factors in energy consumption in this field. Most influential authors, countries, and journals in this area were identified. This analysis contributes academically, by mapping research trends and providing guidance for future studies; practically, by offering insights for educational administrators on common factors affecting energy consumption; and in terms of policy, by advocating for the promotion of social sciences-based investigations on the topic.

A Study on the Measurement of Regional Energy Consumption Efficiency and Decomposition of Its Influencing Factors in China: New Evidence for Achieving SDGs

With the growth of global population and economic development, people are facing the problem of increasing scarcity of renewable energy and unsustainable energy use. To achieve the sustainable development goals (SDGs) proposed by the United Nations, research on energy consumption efficiency has become particularly important. This research evaluates the energy consumption efficiency of 270 cities in China through an improved EBM model and finds a common phenomenon of low energy consumption efficiency in the cities, with the highest efficiency in northeast China and the lowest efficiency in eastern China. In addition, the efficiency of industrial exhaust emissions most significantly positively correlates with the efficiency of employed population and total energy consumption efficiency, while the efficiency of regional GDP does not significantly correlate with the efficiency of the two input variables. Using the LMDI method to decompose the driving factors of energy consumption efficiency in the cities, we find that the most important factor affecting energy consumption efficiency is their own energy endowment. Therefore, to improve the energy consumption efficiency of its cities, the China government should comprehensively consider factors such as regional economic development level, industrial structure, and technological level differences, formulate relevant energy-saving and emission-reduction policies, focus on optimizing the energy consumption structure, encourage technological progress and innovation, and help increase investment in science and technology.

ЭНЕРГОМЕНЕДЖМЕНТ В МЕХАТРОННЫХ СИСТЕМАХ: ОПТИМИЗАЦИЯ ЭНЕРГОПОТРЕБЛЕНИЯ РОБОТОВ

The article discusses methods for optimizing energy consumption in mechatronic sys-tems, with a special emphasis on robotics. Energy management strategies are presented, including en-ergy-optimized trajectory planning, dynamic energy management, and architectural approaches to sys-tem design. An analysis of the factors affecting energy consumption is carried out, and solutions are proposed to improve energy efficiency without compromising performance. The results demonstrate that the proposed methods can significantly reduce energy consumption, increasing the autonomous opera-tion time of robots. In conclusion, possible directions for further research in the field of integrating ma-chine learning methods and hybrid approaches to energy management are discussed.

Study on prediction and influencing factors of energy consumption in Jiangxi province

This study aims to determine the main factors affecting energy consumption in Jiangxi Province, and to forecast energy consumption effectively. An indicator system of energy consumption influencing factors containing 17 variables is constructed from 4 aspects of economy, society, technology and energy. Based on relevant data from 1996 to 2021, conduct comprehensive analysis and select 10 main factors. Establish three energy consumption prediction models: multiple regression model (MLR), time series model (ARIMA), and support vector regression (SVR). Evaluate the prediction performance through mean square error (MSE), mean absolute percentage error (MAPE), and goodness of fit (R2). The main influencing factors of energy consumption are determined as GDP, output value of the tertiary industry, industrial output value, residents’ consumption level, fixed assets investment of the whole society, total population, urbanization rate, internal expenditure of R&amp;D funds, energy intensity, and energy consumption structure. The best prediction model is SVR model. It provides a basis for the effective formulation of energy policies and the implementation of economic development programs.

Research on prediction model of electric vehicle thermal management system based on particle swarm optimization- Back propagation neural network

The variation of temperature in various components of the Electric Vehicle Thermal Management (EVTM) system is the main factor affecting energy consumption. A good temperature prediction method is a prerequisite for optimizing energy strategies. The temperature variation of each component in the thermal management system is influenced by various factors, such as ambient temperature, vehicle speed, and air conditioning compressor speed. Therefore, this paper proposes a temperature prediction method based on Particle Swarm Optimization (PSO) and Back propagation Neural Network (BP). The PSO-BP method is used to update the weights and thresholds of the neural network. Real-time data collected from road experiments are used to ensure the accuracy of the influencing factors. Simulation results show that compared to the BP neural network, the proposed PSO-BP prediction method reduces the prediction errors by 66%, 75%, and 25% respectively.

Exploring factors affecting household energy consumption in the internet era: Empirical evidence from Chinese households

Household sector is the second largest energy consumption sector after industrial sector. With the increasing popularity of the Internet, empirical studies based on large-scale households to study factors affecting household energy consumption are quite rare. This paper integrates Internet behaviors into structural equation model based on the data of Chinese household energy consumption survey in an effort to find out the factors affecting household energy consumption and explore the relationship between these factors. According to the findings of this study, the increased frequency of using Internet has mitigated the positive effect of income on household energy consumption to a certain extent. This paper also comes to some interesting conclusions. For example, the understanding of energy conservation policies has significantly increased the energy consumption of Chinese households. Moreover, different regions and different types of households are faced with different factors affecting energy consumption. With reference to the characteristics of Chinese households, this paper discusses the possible underlying cause of these conclusions, which is of great significance for Chinese government to formulate targeted energy conservation policies and provides a reference for other countries.

Analysis of energy consumption prediction for office buildings based on GA-BP and BP algorithm

To gain building energy consumption information during the design phase, the variance analysis to identify significant factors affecting energy consumption in China cold-region office buildings are carried out in this study. Key factors are selected, and prediction models for energy consumption in cold-region office buildings are established using BP and GA-BP algorithms. Three prediction model evaluation indexes are introduced to evaluate the prediction accuracy of the models. The results show that the maximum RMSE of the BP neural network prediction model is 0.498, and the maximum MAPE is 0.797%. Furthermore, the GA algorithm is used to optimize the BP neural network, resulting in a prediction model with a maximum RMSE of 0.359 and a maximum MAPE of 0.289%. The prediction accuracy of the GA-BP algorithm is better than that of the BP algorithm.

Investigation into energy performance of a multi-building complex in a hot and humid climate: efficacy of energy saving measures

PurposeCommercial buildings, which include office buildings, are one of the three major energy-consuming sectors, alongside industrial and transportation sectors. The vast increase in the number of buildings is a positive sign of the rapid development of Malaysia. However, most Malaysian government office buildings tend to consume energy inefficiently due to lack of energy optimization. Most of the previous studies focused on the performance of green buildings in fulfilling the green development guidelines. As such, it is essential to study the energy performance of existing government office buildings that were constructed before most energy-efficient standards were implemented to mitigate energy wastage due to the lack of energy optimization. This study aims to analyse the energy performance of existing non-green Malaysian government office buildings and the factors that influence building energy consumption, as well as to evaluate the efficacy of the existing energy conservation measures.Design/methodology/approachThis study was conducted by a literature review and case study. The chosen buildings are six government office building blocks located in Kuala Lumpur, the capital city of Malaysia. In this study, a literature review has been conducted on the common factors affecting energy consumption in office buildings. The energy consumption data of the buildings were collected to calculate the building energy intensity (BEI). The BEI was compared to the MS1525:2019 and GBI benchmarks to evaluate energy performance. SketchUp software was utilized to illustrate the solar radiation and sun path diagram of the case study buildings. Finally, recommendations were derived for retrofit strategies based on non-design factors and passive design factors.FindingsIn typical government office buildings, the air-conditioning system consumed the most energy at 65.5%, followed by lighting system at 22.6%, and the remaining 11.9% was contributed by office appliances. The energy performance of the case study buildings is considered as satisfactory as the BEI did not exceed the MS1525:2019 benchmark of 200 kWh/m2/year. The E Block recorded the highest BEI of 183.12 kWh/m2/year in 2020 due to its north-east orientation which is exposed to the most solar radiation. Besides, E Block consists of rooms that can accommodate large number of occupants. As such, non-design factors which include higher occupancy rate and higher cooling demand due to high outdoor temperature leads to higher energy consumption. By considering passive design features such as building orientation and building envelope thermal properties, energy consumption can be reduced significantly.Originality/valueThis study provided a comprehensive insight into the energy performance of Malaysian government office buildings, which were constructed before the energy-efficient standards being introduced. By calculating the BEI of six government office buildings, it is found that the energy performance of the case study buildings fulfils the MS1525 benchmark, and that all their BEIs are below 200 kWh/m2/year. Malaysia's hot and humid climate significantly affects a building's cooling load, and it is found the air-conditioning system is the major energy consumer of Malaysian government office buildings. This study discusses the efficacy of the energy-saving measures implemented in the case study buildings to optimize energy consumption. Recommendations were derived based on the non-design factors and passive design factors that affected the energy consumption of the case study building. It is envisioned that this study can provide practical strategies for retrofit interventions to reduce energy consumption in Malaysian office buildings as well as for office buildings that are in a similar climate.

Energy Consumption and Carbon Emissions of Nearly Zero-Energy Buildings in Hot Summer and Cold Winter Zones of China

Issues of energy efficiency and sustainability in buildings are gaining increasing attention in the context of the “3060” dual-carbon initiative. In recent years, nearly zero-energy buildings (nZEBs) have emerged as a potentially viable solution to the challenges of the energy crisis in the building sector, and it is important to study the factors influencing their energy consumption and carbon emissions. However, existing research lacks analyses of multifactor interactions, and the problem of high energy consumption has not been sufficiently addressed. Taking a typical residential building in the Yangtze River basin as the study subject, this study, jointly funded by the University of Nottingham and Hubei University of Technology, proposes a hybrid approach that combines building energy simulation and orthogonal experiments to investigate factors pertaining to buildings, people, and the environment to identify key influencing factors and explore the energy consumption and carbon emission characteristics of residential buildings in hot summer and cold winter (HSCW) zones. Our findings reveal the following: (1) The use of renewable energy sources, such as solar photovoltaic power generation and solar hot water, and renewable energy systems such as ground-source heat pumps, in the operation phase of a baseline building can result in a 61.76% energy-saving and a 71% renewable energy utilization rate. (2) To more easily meet the requirements of nZEB standards, it is recommended to keep KE within the range of 0.20–0.30 W/(m2·K), KR within the range of 0.15–0.20 W/(m2·K), and VT within the range of 0.6–0.7 h−1. This study will help to identify the critical factors affecting energy consumption and provide a valuable reference for building energy efficiency in HSCW zones.

FORECASTING THE CONSUMPTION OF FUEL AND ENERGY RESOURCES FOR MILITARY UNITS IN THE ARCTIC ZONE OF THE RUSSIAN FEDERATION

The Russian Arctic is one of the promising economic zones with a rich supply of natural resources. In a world in which territorial divisions, ideological rivalries, ethnic intolerance, and even nuclear proliferation can theoretically be normalized without the use of military force, it is the wealth of energy resources that remains the last casus belli that can lead to catastrophic consequences. That is why the Armed Forces of the Russian Federation are faced with the task of protecting state sovereignty and maintaining security in the Arctic zone. To maintain the combat capability and readiness of a military facility, it is necessary to maintain stocks of fuel and energy resources at the required level. Forecasting the consumption of fuel and energy resources is the most important task. This methodology takes into account factors of various types: periodic (cyclical) and random, including those inherent in military production. All factors affect energy consumption to varying degrees.&#x0D; Cyclical factors are the most predictable and have a greater impact on the consumption of fuel and energy resources. Although random factors occupy a small share in the process of energy consumption, the amplitude of deviations can be quite significant.&#x0D; The methodology makes it possible to determine the consumption of fuel and energy resources in the context of the increasing complexity of the relationship between the economy and energy and the growing uncertainty of the future development of military units and military bases in the Arctic zone of the Russian Federation based on the applied stochastic differential equation.&#x0D; Purpose. Develop a methodology for forecasting the consumption of fuel and energy resources for military units in the Arctic zone of the Russian Federation.&#x0D; Methodology in article economic-mathematical methods, and also statistical methods.&#x0D; Results: the most informative parameters were obtained, showing some aspects of the process of forecasting the consumption of fuel and energy resources.&#x0D; Scope of the results: it is advisable to apply the results obtained in the apartment-operational subdivisions of the logistics management bodies of the military districts to obtain predicted values of the consumption of fuel and energy resources (TER)at military infrastructure facilities (military camps).&#x0D; Practical implications it is expedient to apply the received results the economic subjects which are carrying out foreign economic activity, one of which elements are export operations.