Energy Consumption Factors Research Articles

Identification of influential factors for combined energy consumption and indoor environmental quality in residential buildings

The development of an overall building performance simulation model requires a multitude of input parameters which can be a challenging and resource-heavy task for building modellers. Furthermore, some parameters have little impact on a building’s overall performance and contribute little towards model prediction accuracy. Feature selection has been employed to identify the most influential input parameters to reduce complexity and computational time. However, previous studies focused mainly on identifying parameters that impact energy consumption in residential buildings, neglecting the important relationship between energy consumption and indoor environmental quality (IEQ). Therefore, this study proposes a novel simulation framework that integrates occupancy-based building archetypes, parametric simulation, and machine learning techniques to develop an overall building performance prediction model. Using this framework, the study generates a synthetic dataset of 40,000 simulations and performed embedded feature selection using two machine learning algorithms, Random Forest (RF) and Gradient Boosting Technique (GBT), to identify parameters that impact heating energy consumption, thermal discomfort hours, and CO2 concentration simultaneously. The results demonstrate that the ranking for importance and the number of required parameters vary depending on the target variable. Also, the set of parameters for combined analysis differs from individual target variable analysis. The GBT algorithm with embedded feature selection provides the most accurate prediction results with lower root mean square error (RMSE) and absolute error (AE) for individual and combined analyses. This study provides valuable insights for accurate parameter selection and analysis of overall building performance.

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.

Effect of permeable spacer structure on energy loss and mass transfer in reverse osmosis membrane modules

This study used numerical simulation to investigate the effects of a novel permeable cylindrical-flow distributor on energy loss and mass transfer in reverse osmosis (RO) membrane modules. The analysis focused on variables, such as Reynolds number, inlet concentration and flow incidence angle, influencing the improvement of water production efficiency in RO membranes and the reduction of concentration polarisation on the membrane surface. The membrane surface shear stress, vorticity, permeate flux and concentration polarisation factor were used to describe these effects. To better explain the performance of the permeable flow distributor, the viscous resistance coefficient (Du) in the porous media model was used to measure the permeability of the flow distributor. Furthermore, a produced water energy consumption factor (JCP) was defined to represent the energy required for unit water production. The results indicate that the permeable flow distributor significantly reduces pressure loss in the membrane channels, avoiding the high energy consumption associated with traditional flow distributors in RO processes. The Du exhibits an optimal value in the range of 1e8 to 1e10, which maximises the JCP, leading to a 32.9 % improvement compared to traditional impermeable flow distributors. This achievement enables energy-efficient and effective desalination of seawater.

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.

Analysis of Influencing Factors of Green Building Energy Consumption Based on Genetic Algorithm

Analysis of Influencing Factors of Green Building Energy Consumption Based on Genetic Algorithm

Investigating the influential factors in real-world energy consumption of battery electric vehicles

The transportation industry is undergoing a major shift towards electrification to mitigate carbon emissions and decrease reliance on fossil fuels in response to global climate change and greenhouse gas concerns. Recently, battery electric vehicles (BEVs) have made significant progress and are becoming a more viable option for achieving zero-emission transportation. The aim of this study is to investigate the energy consumption patterns of BEVs operating in real-world driving scenarios encompassing various route conditions. The vehicle sensor data employed in this study was acquired through onboard diagnostic devices that directly gathered raw data and subsequently transmitted it to mobile applications. Various significant factors, such as payload, road slope level, speed range, acceleration, and loads of heating, ventilation, and air conditioning, are considered as variables influencing energy consumption. By utilizing the large amount of data collected, machine learning (ML) techniques were applied to develop a predictive model of energy consumption and identify variables that influence energy consumption. The outcomes of this study hold the potential to offer guidance to transportation policymakers and furnish valuable insights for prospective buyers considering BEVs. Furthermore, the application of ML in the development of a predictive model demonstrated efficacy and exhibits promising potential for wider-ranging applications.

Do renewable energy and total factor productivity eliminate CO2 emissions in Turkey?

This paper analyzes the impact of renewable energy consumption, total factor productivity (TFP), income, imports, and exports on consumption-based CO2 emissions in Turkey from 1990 to 2019, utilizing the Autoregressive Distributed Lagged (ARDL) method. The findings indicated that the use of renewable energy, total factor productivity and exports negatively influence CO2 emissions. On the other hand, the positive impact of income and imports on CO2 emissions was found. The negative impact of renewable energy consumption and total factor productivity highlights the importance of implementing environmentally friendly measures in the energy sector (particularly raising the proportion of renewable energy in overall energy consumption) and technological innovation.

Analysis of influencing factors of energy consumption in Beijing: based on the IPAT model

Analysis of influencing factors of energy consumption in Beijing: based on the IPAT model

Predictive model of pump energy consumption based on image feature extraction and instance-based transfer learning

The energy consumption of pumps for water supply accounts for a high proportion of the total global energy consumption. The analysis of the pump's energy consumption factors and the construction of prediction models play a crucial role in carbon emission reductions throughout the water supply process. However, difficulties in accurately constructing input features under complex variables and the scarcity and anomalies of monitoring data during system operation pose great challenges to accurate energy consumption prediction. In order to address this issue, a water pump energy consumption prediction method based on image feature extraction and instance transfer is proposed, using the water pump systems installed in two residential communities as the research objects. Firstly, a novel image-based correlation determination method for feature engineering was introduced and validated on a sufficient dataset. Subsequently, in the context of poor data transfer learning, a water pump energy consumption prediction model, named Two-stage TrAdaBoost.R2 (TSTR), was established by incorporating the image correlation determination method and fine-tuning the model parameters using the particle swarm optimization algorithm to enhance prediction accuracy. The experimental results demonstrated the effectiveness of the proposed image-based correlation determination method, showing a 5.49%–44.33% improvement in the coefficient of variation of root mean square error (CV-RMSE) prediction accuracy compared to classical time-series correlation determination methods. Furthermore, in the poor data transfer prediction experiment, the TSTR-based model achieved 0.90% and 20.22% higher CV-RMSE prediction accuracy compared to two comparative models. These findings confirmed the effectiveness of instance transfer learning methods in poor data scenarios and provided valuable insights for selecting input feature construction methods under complex variables.

Fr-Aro: Secure interference aware fuzzy based clustering and hybrid optimization driven data routing in VANETs

Vehicular ad hoc network (VANET) is composed of a group of nodes or vehicles that enable communication with other connected vehicles despite a lack of infrastructure. To design a reliable routing protocol is a major challenge for ensuring effective data communication among vehicles. This research develops a fuzzy logic concept to cluster the nodes and a Cluster Head is selected based on the fitness constraints. Thereafter, the optimal path used to route the data packets are selected using the proposed Fr-ARO based on the fitness, like energy consumption, delay, distance, Signal-to-Interference Ratio (SIR), entropy-weighted model, and trust factors. Some of the trust parameters considered for the routing mechanism are direct trust, indirect trust, historical trust, recent trust, and availability factor. Moreover, the optimal path is computed by the developed Fr-ARO algorithm that is devised through the incorporation of Aquila Optimizer (AO) with Remora Optimization Algorithm (ROA). The proposed model attains higher performance with the metrics, like energy, trust, distance, and SIR of 0.5744 J, 0.6123, 3.062 m, and 38.4674.

The dynamic relationships between nuclear energy consumption, nuclear reactors and load capacity factor: time and frequency domain panel data analysis.

Nuclear energy is an environmental issue that needs to be carefully considered regarding its consequences. The environmental effects of nuclear energy encompass a complex interplay of factors, ranging from managing radioactive waste to the potential for accidents, emissions, and resource depletion, necessitating a thorough examination of its impact on our planet. This study examines the relationships between nuclear energy, nuclear fission, nuclear fusion, and load capacity factor for the eight countries (Canada, China, France, Germany, Japan, Russia, Spain, USA) that consumed the most nuclear energy from 1993 to 2018 using the time (Emirmahmutoglu & Kose, 2011) and frequency domain (Croux & Reusens, 2013) panel causality tests. The results differ according to time and frequency domain causality tests. While the time-domain causality test results showed no causal relationship between the nuclear energy consumption, nuclear reactors, and the load capacity factor variables, the frequency-domain causality test revealed that there are causal relationships between these variables in the short, intermediate and long run. According to the frequency-domain causality test results, there is a bidirectional causality relationship between nuclear reactors, energy consumption and load capacity factor. In line with the main findings of this study, it is recommended that these countries implement effective policies to increase environmental quality and make investments and incentives in nuclear energy and technologies.

Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length.

How cereal yield is influenced by eco-environmental factors? ARDL and spectral causality analysis for Turkey

Food crop production and energy consumption scenarios through environmental factors are a critical concern for humanity. To this end, this study focuses on cereal yield in Turkey influenced by climate change, energy consumption, CO2 emissions, and eco-environmental factors. The objective is finding evidence for an influence level of cereal yield through its determiners, which has not been effectively discussed in literature yet. To fill the gap, a time-series autoregressive distributed lag (ARDL) analysis and spectral causality relationships are conducted in the short-run, medium-run, and long-run. Data is collected annually by various institutions, from 1960 to 2019. The ARDL findings reveal that there is a cointegration between the variables and a long-run correlation between cereal yield and eco-environmental variables. While, energy consumption positively affects cereal yield, climate change has varying effects, and CO2 emissions negatively affect it. This study shows that environmental and economic factors in Turkey are critical for cereal yield. The Turkish government should encourage agriculture incentives for productive cereal supply. Increasing renewable energy consumption can be a powerful strategy in Turkey not only for increasing cereal yield but also for the negative influences of climate change. Therefore, the used economic model can serve an example for any countries.

Energy efficiency optimization and carbon emission reduction targets of resource-based cities based on BiLSTM-CNN-GAN model

IntroductionEnergy consumption and carbon emissions are major global concerns, and cities are responsible for a significant portion of these emissions. To address this problem, deep learning techniques have been applied to predict trends and influencing factors of urban energy consumption and carbon emissions, and to help formulate optimization programs and policies.MethodsIn this paper, we propose a method based on the BiLSTM-CNN-GAN model to predict urban energy consumption and carbon emissions in resource-based cities. The BiLSTMCNN-GAN model is a combination of three deep learning techniques: Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN). The BiLSTM component is used to process historical data and extract time series information, while the CNN component removes spatial features and local structural information in urban energy consumption and carbon emissions data. The GAN component generates simulated data of urban energy consumption and carbon emissions and optimizes the generator and discriminator models to improve the quality of generation and the accuracy of discrimination.Results and discussionThe proposed method can more accurately predict future energy consumption and carbon emission trends of resource-based cities and help formulate optimization plans and policies. By addressing the problem of urban energy efficiency and carbon emission reduction, proposed method contributes to sustainable urban development and environmental protection.

Impact of eco-innovation and financial efficiency on renewable energy – Evidence from OECD countries

The research on the driving factors of renewable energy consumption has attracted a lot of attention, but the impact of eco-innovation and financial efficiency on it is rare, therefore, we launched this study to explore the impact of financial efficiency and ecological innovation on renewable energy consumption in OECD countries between 2001 and 2018 using fully modified ordinary least square (FMOLS) and Driscoll-Kraay standard error regression. Empirical results show that both financial efficiency and eco-innovation have long-term positive correlations with renewable energy consumption. In addition, we analyzed the heterogeneity of the impact of selected factors on renewable energy consumption through fixed-effects panel quantile regression, and found that with the increase of quantile, the promotion effect of financial efficiency and ecological innovation on renewable energy consumption is decreasing. Finally, we suggest that OECD countries increase and expand their total financial resources, while promoting healthy competition among financial institutions to improve financial efficiency; promote ecological innovation in energy production, transmission and distribution, in order to improve the use of renewable energy.

Assessing the connection between competitive industrial performance on load capacity factor within the LCC framework: Implications for sustainable policy in BRICS economies.

Industrialization plays a crucial role in socio-economic development as it holds significant potential for creating new jobs, tightening the income gap, and promoting the use of advanced technology. As global competition intensifies, emerging economies emulate industrialized economies in accelerating manufacturing activity to improve national welfare and join the new global order. However, policymakers' understanding of how competitiveness in the industrial sector helps developing countries accomplish their sustainable development goals must be deepened. This paper aims to analyze the connections among competitive industrial performance, renewable energy consumption, urbanization, and load capacity factor (LCF) in the BRICS economies for the period between 1990 and 2018. Robust evidence from the continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) models shows that greater industrial competitiveness enhances environmental quality. The findings also reveal that income growth ultimately evolves as an ecologically friendly factor, confirming the validity of the load capacity curve (LCC) hypothesis. Another outcome of the econometric analysis indicates that renewable energy consumption contributes to the LCF, whereas urbanization damages the environment. Therefore, BRICS policymakers should concentrate on maintaining their competitiveness, implementing resilient urban planning, and promoting the usage of renewable energy to safeguard the environment while simultaneously achieving rapid economic growth.

Utilizing Dynamic Analysis in the Complex Design of an Unconventional Three-Wheeled Vehicle with Enhancing Cornering Safety

Current trends in the transportation industry prioritize competitive rivalry, compelling manufacturers to prioritize concepts such as quality and reliability. These concepts are closely associated with public expectations of safety, vehicle lifespan, and trouble-free operation. However, the public must recognize that a vehicle weighing several hundred kilograms, moving at a non-zero speed, only contacts the road surface through a few points (depending on the number of wheels), each no larger than a human palm. Therefore, it is imperative to operate the vehicle in a manner that optimizes the behavior of these contact points. There are situations where drivers find themselves requiring dynamic vehicle handling, often unpredictable with a high degree of uncertainty. Rapid changes in direction become necessary in these cases. Such maneuvers can pose a significant risk of rollover for three-wheeled vehicles. Hence, the vehicle itself should contribute to increased ride safety. This paper presents key findings from the development of an unconventional three-wheeled vehicle utilizing the delta arrangement. Rollover safety for three-wheeled vehicles is currently well-managed, thanks to the utilization of electronic or mechatronic systems in delta-type vehicles to enhance stability. However, these systems require additional components. In contrast, the proposed control system operates solely on a mechanical principle, eliminating operational costs, energy consumption, maintenance expenses, and similar factors. The study also explores the absence of equivalent suspension and steering systems for front-wheel steering. Such designs are lacking in both practical applications and theoretical realms. Analytical and simulation calculations are compared in this study, highlighting the effectiveness of the newly proposed control system in enhancing stability and safety compared to conventional front-wheel suspension systems. Simulation programs provide more realistic results than analytical calculations due to their ability to account for dynamic effects on vehicle components and passengers, which is practically unfeasible in analytical approaches. Furthermore, this study focuses on investigating the fatigue life of material frames subjected to dynamic loading, which is a crucial aspect of ensuring safety. It is essential to have various testing devices to examine the fatigue life of materials under both uniaxial and multiaxial loading conditions. However, obtaining experimental results for fatigue life measurements of specific materials, which can be directly applied to one’s research, poses significant challenges. Hence, the proposed testing device plays a vital role in measuring material fatigue life and advancing the development of unconventional transportation methods. The information about the original testing device aligns perfectly with the article’s emphasis on dynamic analysis. The ultimate objective of all these efforts is to put the vehicle into practical operation for commercial utilization.

Does Daylight Saving Time in the Southern United States Save Energy? – Evidence from a Natural Experiment in Arizona

Abstract This paper investigates why Arizona does not implement Daylight Saving Time based on energy consumption in the Southern states of the United States. Daylight Saving Time is a practice of adjusting clocks to extend daylight during the day, which can affect energy usage and conservation. While Daylight Saving Time is commonly believed to save energy, Arizona has been exempt from it since 1968, citing concerns of increased energy consumption due to longer daytime. This study employs synthetic control to construct a synthetic Arizona with Daylight Saving Time and compare it with Arizona without Daylight Saving Time. This paper provides evidence supporting the Daylight Saving Time exemption in Arizona by analyzing state-level panel data on energy consumption and related factors.

Exploring the mTOR Signalling Pathway and Its Inhibitory Scope in Cancer.

Mechanistic target of rapamycin (mTOR) is a protein kinase that regulates cellular growth, development, survival, and metabolism through integration of diverse extracellular and intracellular stimuli. Additionally, mTOR is involved in interplay of signalling pathways that regulate apoptosis and autophagy. In cells, mTOR is assembled into two complexes, mTORC1 and mTORC2. While mTORC1 is regulated by energy consumption, protein intake, mechanical stimuli, and growth factors, mTORC2 is regulated by insulin-like growth factor-1 receptor (IGF-1R), and epidermal growth factor receptor (EGFR). mTOR signalling pathways are considered the hallmark in cancer due to their dysregulation in approximately 70% of cancers. Through downstream regulators, ribosomal protein S6 kinase β-1 (S6K1) and eukaryotic translation initiation factor 4E binding protein 1 (4E-BP1), mTORC1 influences various anabolic and catabolic processes in the cell. In recent years, several mTOR inhibitors have been developed with the aim of treating different cancers. In this review, we will explore the current developments in the mTOR signalling pathway and its importance for being targeted by various inhibitors in anti-cancer therapeutics.

Investigating spatio-temporal characteristics and influencing factors for green energy consumption in China

The green transformation of energy consumption is beneficial for promoting green development in China. This study constructed a green energy consumption evaluation index system and measured the green energy consumption levels in 30 provinces of China from 2000 to 2019 using the fuzzy comprehensive evaluation method. This study further employed the spatial Durbin model to examine influencing factors and spillover effects of green energy consumption. The results showed that, temporally, China’s green energy consumption levels had a fluctuating upward trend. While, spatially, the overall levels of green energy consumption in China showed apparent characteristics of “high in the west and low in the east”. In terms of influencing factors, environmental regulations played an important role in promoting green energy consumption in the region, while economic development, opening up, and industrial structure had considerably inhibiting effects. Additionally, economic development, opening up, and industrial structure of neighboring regions showed marked positive spillover effects, while urbanization level and technological innovation showed substantial negative spillover effects. The regional heterogeneity test results showed that environmental regulation and industrial structure rationalization were the important factors for promoting green energy consumption in the eastern region, environmental regulation played an important driving role in the central region, and opening to the outside world and technological innovation helped improve the level of green energy consumption in the western region.