scholarly journals Differences in Energy Consumption in Electric Vehicles: An Exploratory Real-World Study in Beijing

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Kezhen Hu ◽  
Jianping Wu ◽  
Tim Schwanen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Real World ◽  
Time Series Data ◽  
Urban Traffic ◽  
Environmental Benefits ◽  
Series Data ◽  
Traffic Conditions ◽  
Infrastructure Design

Electric vehicles (EVs) are widely regarded as a promising solution to reduce air pollution in cities and key to a low carbon mobility future. However, their environmental benefits depend on the temporal and spatial context of actual usage (journey energy efficiency) and the rolling out of EVs is complicated by issues such as limited range. This paper explores how the energy efficiency of EVs is affected and shaped by driving behavior, personal driving styles, traffic conditions, and infrastructure design in the real world. Tests have been conducted with a Nissan LEAF under a typical driving cycle on the Beijing road network in order to improve understanding of variations in energy efficiency among drivers under different urban traffic conditions. Energy consumption and operation parameters were recorded in both peak and off-peak hours for a total of 13 drivers. The analysis reported in this paper shows that there are clear patterns in energy consumption along a route that are in part related to differences in infrastructure design, traffic conditions, and personal driving styles. The proposed method for analyzing time series data about energy consumption along routes can be used for research with larger fleets of EVs in the future.

Effects of Energy Prices on Energy Efficiency in China

2014 ◽  
Vol 962-965 ◽  
pp. 1767-1772
Author(s):  
Zun Ming Ren
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Industrial Structure ◽  
Time Series Data ◽  
Series Data ◽  
Energy Prices ◽  
Electricity Prices ◽  
Consumption Structure ◽  
Integration Test

The paper utilized the co-integration test, error correction model and Granger causality test, and other methods to verify the influence of the coal, oil and electricity prices, industrial and energy consumption structures on China's energy efficiency based on time-series data from 1979 to 2010. Test results show that: there is long-term equilibrium relationship of the energy prices, industrial structure, energy consumption structure and energy efficiency; coal prices, industrial structure and energy consumption structure are the Granger reasons of energy efficiency both in the short and long run; while the oil and electricity prices only constitute the long-term Granger reasons of energy efficiency. Finally, it analyzed the implications of policies of the empirical results and provided some constructive suggestions.

scholarly journals Exploring the Energy Efficiency of Electric Vehicles with Driving Behavioral Data from a Field Test and Questionnaire

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kezhen Hu ◽  
Jianping Wu ◽  
Mingyu Liu
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Field Test ◽  
Electric Vehicles ◽  
Field Tests ◽  
Driving Behavior ◽  
Future Research ◽  
Driving Style ◽  
Traffic Conditions ◽  
Energy Consumption Prediction

With increasing concerns about urban air quality and carbon emissions, electric vehicles (EVs) have gained popularity in megacities, especially in Europe and Asia. The energy consumption of EVs has subsequently caught researchers’ attention. However, the exploration of energy consumption of EVs has largely focused on people’s revealed driving behavior and rarely touched on their self-perception of driving styles. In this paper, we developed a more human-centric approach, aiming to investigate how the energy efficiency of EVs is shaped by the driving behavior and driving style in the urban scenario from field test data and driving style questionnaires (DSQs). Field tests were carried out on a designated route for a total of 13 drivers in the city of Beijing, where vehicle operation parameters were recorded under both congested and smooth traffic conditions. DSQs were collected from a larger pool of drivers including the field test drivers to be applied to driving style factor analysis. The results of a correlation analysis demonstrate the dynamic interaction between drivers’ revealed behavior and stated driving style under different traffic conditions. We also proposed an energy consumption prediction model with the fusion of collected driving parameters and DSQ data and the result is promising. We hope that this study would draw inspiration for future research on people’s transitioning driving behavior in an electric-mobility era.

CAUSAL RELATIONSHIP OF ENERGY CONSUMPTION AND ECONOMIC GROWTH – A CASE STUDY OF PAKISTAN

2021 ◽  
Vol 9 (1) ◽  
pp. 139-164
Author(s):  
Saddam Hussain ◽  
Chunjiao Yu
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Inflation Rate ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Series Data ◽  
Relationship Of

This paper explores the causal relationship between energy consumption and economic growth in Pakistan, applying techniques of co-integration and Hsiao’s version of Granger causality, using time series data over the period 1965-2019. Time series data of macroeconomic determi-nants – i.e. energy growth, Foreign Direct Investment (FDI) growth and population growth shows a positive correlation with economic growth while there is no correlation founded be-tween economic growth and inflation rate or Consumer Price Index (CPI). The general conclu-sion of empirical results is that economic growth causes energy consumption.

scholarly journals PENGARUH KONSUMSI ENERGI DAN REZIM TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA

2019 ◽  
Vol 1 (2) ◽  
pp. 401
Author(s):  
Zakiah Husna ◽  
Idris Idris
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Renewable Energy Consumption ◽  
Energy Regime ◽  
Negative Effect ◽  
Positive Effect

This study aims to determine the effect of energy consumption and regime on economic growth in Indonesia. The data used is secondary data in the form of time series data from 1988-2017, with documentation and library study data collection techniques obtained from relevant institutions and agencies. the variables used are economic growth (GDP), non-renewable energy consumption, renewable energy consumption and regime, the research methods used are: (1) Multiple Regression Analysis (OLS), (2) Classical Assumption Test results of research stating that: ( 1) non-renewable energy consumption has a positive effect on economic growth in Indonesia. (2) consumption of renewable energy has a positive effect on economic growth in Indonesia. (3) the energy regime has a negative effect on economic growth in Indonesia. (4) non-renewable energy consumption, renewable energy consumption and energy regime have a significant effect on economic growth in Indonesia. so only the energy regime has a negative effect on economic growth in Indonesia.

scholarly journals An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Ruixia Cui ◽  
Wenbo Hua ◽  
Kai Qu ◽  
Heran Yang ◽  
Yingmu Tong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Time Series Data ◽  
Time Window ◽  
Medical Center ◽  
Characteristic Curve ◽  
Series Data ◽  
Gradient Boosting ◽  
Early Management ◽  
Extreme Gradient Boosting

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

scholarly journals Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 773 ◽  
Author(s):  
Muhammad Fahim ◽  
Alberto Sillitti
Keyword(s):  
Energy Consumption ◽  
Time Series Data ◽  
Residential Buildings ◽  
Series Data ◽  
Smart Meters ◽  
Learning Mechanism ◽  
Efficient Energy ◽  
Household Characteristics ◽  
Univariate Time Series ◽  
Consumption Data

The increasing penetration of smart meters provides an excellent opportunity to monitor and analyze energy consumption in residential buildings. In this paper, we propose a framework to process the observed profiles of energy consumption to infer the household characteristics in residential buildings. Such characteristics can be used for improving resource allocation and for an efficient energy management that will ultimately contribute to reducing carbon dioxide (CO 2 ) emission. Our approach is based on automated extraction of features from univariate time-series data and development of a model through a variant of the decision trees technique (i.e., ensemble learning mechanism) random forest. We process and analyzed energy consumption data to answer four primitive questions. To evaluate the approach, we performed experiments on publicly available datasets. Our experiments show a precision of 82% and a recall of 81% in inferring household characteristics.

scholarly journals The Financial Development-Renewable Energy Consumption Nexus in the Case of Azerbaijan

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6265
Author(s):  
Shahriyar Mukhtarov ◽  
Sugra Humbatova ◽  
Natig Gadim-Oglu Hajiyev ◽  
Sannur Aliyev
Keyword(s):  
Economic Growth ◽  
Renewable Energy ◽  
Energy Consumption ◽  
Financial Development ◽  
Time Series Data ◽  
Policy Implications ◽  
Small Samples ◽  
Series Data ◽  
Energy Prices ◽  
Renewable Energy Consumption

This article analyzed the relationship between financial development, renewable energy consumption, economic growth, and energy prices in Azerbaijan by employing time series data for the time span of 1993–2015. The autoregressive distributed lagged (ARDL) technique was applied in empirical estimations, because it performs better than all the alternative techniques in small samples, which was the case here in this article. The results of estimation found that there is a positive and statistically significant influence of financial development and economic growth on renewable energy consumption, whereas the prices of energy proxied by CPI have an adverse impact on renewable energy consumption in Azerbaijan. Also, estimation results demonstrated that a 1% rise in financial development, proxied by domestic credit as a percentage of GDP, and economic growth increase renewable energy consumption by 0.16% and 0.60%, respectively. The different financial development impacts on renewable energy consumption and related policy implications were also introduced.

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