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 Empirical Analysis Between Economic Growth and Energy Consumption Based on the Perspective of Industrial Structure

2014 ◽  
Vol 7 (1) ◽  
pp. 158-163
Author(s):  
Shu Yehong ◽  
He Yucheng
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Energy Consumption ◽  
Energy Supply ◽  
Industrial Structure ◽  
Time Series Data ◽  
Supply And Demand ◽  
Hunan Province ◽  
Series Data ◽  
The Relationship

Energy is an important material basis for social and economic development. With the rapid economic development, energy is increasingly becoming an important issue of socio-economic development. As China's "two-oriented society" pilots Hunan Province, a major breakthrough occurred in the transformation of economic growth, adjusting the industrial structure and promoting energy conservation and environmental protection, but still faces shortage of energy supply and demand and supply imbalances, impacting on sustainable economic development. This paper introduces the economic growth based on the previous results and summarized the relationship between energy consumption and classification; also introduces the theory of economic growth, industrial structure and related econometric models providing a theoretical basis and methods of analysis for this study. Secondly, through the relevant data collected and selected first from second and third industry time series data of economic growth and energy consumption of the 1990-2013 year of Hunan Province, the paper established econometric model of industrial structure and the relationship between energy consumption and economic growth in the analysis of the results obtained: the secondary industry is the leading cause of long-term increase in energy consumption which in turn is the driving force of the tertiary industry development. Finally, the relevant suggestions are made for industrial restructuring and for ensuring security of energy supply perspective.

Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search

2019 ◽  
Vol 34 (05) ◽  
pp. 2050010 ◽  
Author(s):  
Peng Zhan ◽  
Changchang Sun ◽  
Yupeng Hu ◽  
Wei Luo ◽  
Jiecai Zheng ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Similarity Search ◽  
High Speed ◽  
High Performance ◽  
Time Series Data ◽  
Rapid Development ◽  
Feature Representation ◽  
Series Data ◽  
Data Points

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.

scholarly journals Optimising the Business and Talent Environment in Chengdu to Help High-quality Economic Development

2021 ◽  
Vol 7 (3) ◽  
pp. p70
Author(s):  
Li Xiang ◽  
Chen Hongxi
Keyword(s):  
Economic Development ◽  
Social Security ◽  
Green Space ◽  
Time Series Data ◽  
Econometric Model ◽  
Series Data ◽  
High Quality ◽  
Environment Policy ◽  
Financial Expenditure ◽  
The Relationship

This paper selects the time series data of Chengdu city from 2009 to 2018, refers to the existing literature, constructs an econometric model for empirical research, uses Eviews software to conduct regression analysis and model testing, and selects the data of its representative variables from the three perspectives of the construction of business talent environment (policy environment, talent attractiveness, and ecological livability level) to conduct an empirical study on the relationship between business talent environment and The results show that: the financial expenditure on social security and employment The results show that social security and employment expenditure, average annual wage and green space in Chengdu, as important factors influencing the construction of business and talent environment, have a significant effect on the high-quality economic development of Chengdu.

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

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 PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals Prediction of Urban and Rural Tourism Economic Forecast Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Wusheng Zhou
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Causal Relationship ◽  
Local Economic Development ◽  
Tourism Industry ◽  
Local Economy ◽  
Series Data ◽  
Economic Forecast ◽  
The Relationship ◽  
Tourism Revenue

With the rapid development of tourism, tourism revenue, as one of the important indicators to measure the development of the tourism economy, has high research value. The quasi-prediction of tourism revenue can drive the development of a series of related industries and accelerate the development of the domestic economy. When forecasting tourism income, it is necessary to examine the causal relationship between tourism income and local economic development. The traditional cointegration analysis method is to extract the promotion characteristics of tourism income to the local economy and construct a tourism income prediction model, but it cannot accurately describe the causal relationship between tourism income and local economic development and cannot accurately predict tourism income. We propose an optimized forecasting method of tourism revenue based on time series. This method first conducts a cointegration test on the time series data of the relationship between tourism income and local economic development, constructs a two-variable autoregressive model of tourism income and local economy, and uses the swarm intelligence method to test the causal relationship and the relationship between tourism income and local economic development, calculate the proportion of tourism industry, define the calculation result as the direct influence factor of tourism industry on the local economy, calculate the relevant effect of local tourism development and economic income, and construct tourism income optimization forecast model. The simulation results show that the model used can accurately predict tourism revenue.

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