Factors Influencing Energy Consumption Research Articles

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.

Factors influencing energy consumption efficiency in greenhouse cropping systems

Factors influencing energy consumption efficiency in greenhouse cropping systems

Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations

Wastewater treatment plants (WWTPs) contribute significantly to the control of pollution in water. However, they are significant energy consumers. Identifying the factors influencing energy consumption is crucial for enhancing the energy efficiency of WWTPs. To address this, the unit energy consumption (UEC) of WWTPs was predicted using machine learning models. In order to accurately evaluate WWTPs' energy utilization efficiency, a comprehensive energy evaluation indicator, UEC (kWh/kg TODremoved) was utilized in this study. Among the prediction models, the eXtreme Gradient Boosting (XGBoost) achieves the highest prediction accuracy. SHapley Additive exPlanations (SHAP) was adopted as the model explanation system, and the results revealed that UEC was negatively affected by TN concentration, which was the most influential factor. The stoichiometry-based model calculation result indicates that the nitrification consumes average 77 % of the overall oxygen demand. SHAP analysis illustrated that the UEC of main technologies decreases with increasing influential factors. Partial dependence plot (PDP) compared average UEC of these technologies and SBR consumed the least amount of energy. The research also indicated that low influent TN concentration is the main problem in China. Consequently, it is imperative to exert efforts in ensuring the influent TN concentration while simultaneously making appropriate adjustments to the treatment process. This study provides valuable implications and methods for retrofitting and upgrading WWTPs.

Decomposition of the site-level energy consumption and carbon dioxide emissions of the iron and steel industry.

Identifying the key factors influencing energy consumption and CO2 emissions is necessary for developing effective energy conservation and emission mitigation policies. Previous studies have focused mainly on decomposing changes in energy consumption and CO2 emissions at the national, regional, or sectoral levels, while the perspective of site-level decomposition has been neglected. To narrow this gap in research, a site-level decomposition of energy- and carbon-intensive iron and steel sites is discussed. In this work, the logarithmic mean Divisia index (LMDI) method is used to decompose the changes in the energy consumption and CO2 emissions of iron and steel sites. The results show that the production scale significantly contributes to the increase in both energy consumption and CO2 emissions, with cumulative contributions of 229.63 and 255.36%, respectively. Energy recovery and credit emissions are two key factors decreasing site-level energy consumption and CO2 emissions, with cumulative contributions to the changes in energy consumption and CO2 emissions of -158.30 and -160.45%, respectively. A decrease in energy, flux, and carbon-containing material consumption per ton of steel promotes direct emission reduction, and purchased electricity savings greatly contribute to indirect emission reduction. In addition, site products and byproducts promote an increase in credit emissions and ultimately inhibit an increase in the total CO2 emissions of iron and steel sites.

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 Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles’ fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle’s energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers’ selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers.

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&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.

Energy consumption assessment and economic analysis of a novel sustainable electro-machining auxiliary system

In contrast to traditional machining methods, electro-machining presents a new opportunity for treating materials that are problematic for conventional approaches. While gaining increasing prominence in the manufacturing industry, electro-machining is marked by heightened energy consumption and reduced operational efficiency. A comprehensive survey of electro-machining manufacturing facilities has identified two pivotal factors influencing energy consumption and efficiency enhancements: 1) determining optimal electrical parameters for electro-machining during the production planning stage, and 2) timely adjustment of electro-machining parameters throughout the production stage. To address these two challenges, an innovative model-based electro-machining auxiliary system has been introduced for the surveillance and optimization of the machining process. The system aims to curtail energy consumption, enhance machining efficiency, and fortify economic viability, all while maintaining machining quality. Notably, the system demonstrates adaptability in deriving optimized electrical parameters tailored to diverse processing conditions. Energy consumption evaluation and techno-economic analysis, employing primary industry data, were conducted to compare the energy efficiency and economic performance of the machining process with the new auxiliary system against the conventional approach. The foremost outcome of adopting the new auxiliary system is a substantial reduction in energy usage; from an economic standpoint, the analysis shows that the cost of machining a single workpiece is reduced by 27.75%; the energy efficiency analysis reveals a 27.75% decrease in energy consumption per unit area, accompanied by an approximately 30% increase in machining efficiency. The proposed approach has demonstrated significant practical value for the sustainable development of the electro-machining process.

The role of intermediate factors in China's energy consumption from the perspective of global production chain

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain.

Consumption Analysis and Optimization Strategies for Four-Bit Absolute-Value Detectors

As embedded systems, communication systems, and neurology research continue to advance, the demand for high-performance components in digital circuits has risen. Simultaneously, there is a growing need for low-energy consumption. In this paper, Four-Bit Absolute-Value Detectors, as crucial components in digital circuits, have garnered significant attention for their role in achieving energy efficiency. This paper embarks on a comprehensive exploration, commencing with the fundamentals of circuit theory, circuit topologies, and circuit styles. Through extensive calculations and comparative studies, this paper conduct a thorough analysis of the factors influencing energy consumption in these circuits. The study reveals that various parameters have a substantial impact on energy consumption. To mitigate this, the author proposes multiple strategies, including gate size optimization, supply voltage optimization, and the selection of appropriate circuit styles. The significance of this study lies in providing foundational insights into the energy optimization of Four-Bit Absolute-Value Detectors. By investigating the interplay between gate sizes, supply voltage, path length, capacitance load, and circuit topology, this paper offer a holistic perspective on energy-efficient design.

Computer vision-based intelligent elevator information system for efficient demand-based operation and optimization

Occupancy and behavior in buildings are considered to be key factors influencing energy consumption. Residential areas contribute substantially to energy consumption in urban environments and have more regular users. Developing methods that can monitor building space utilization patterns and meet occupant comfort requirements is essential for urban energy efficiency. In this work, we propose an innovative computer vision-based Intelligent Elevator Information System (IEIS), which is based on a pipeline framework that sequentially implements elevator door state judgment, passenger attribute recognition, and duplicate-free traffic counting through re-recognition in order to give demand-based operational strategies and recommendations. By monitoring the elevator occupancy on a typical weekday, the elevator operation pattern is classified into three periods including peak period, idle period and balance period, and the demand-driven operation optimization is guided based on their corresponding characteristics. Experiments show that the proposed deep learning approach exhibits high accuracy in all stages of the pipeline. IEIS can facilitate energy-efficient management of elevators, ensure safety, provide human-centered services, and reduce the cost of human intervention while achieving efficient operation, and is expected to migrate to other scenario applications, bringing multiple potential benefits to the society.

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.