scholarly journals INCOME ELASTICITY OF ELECTRIC ENERGY CONSUMPTION FOR DIFFERENT ECONOMIC SECTORS IN COUNTRIES OF THE WORLD

2020 ◽  
Vol 7 (3) ◽  
pp. 170-188
Author(s):  
Andre Assis de Salles ◽  
Ana Beatriz Carvalho Werlang ◽  
Illana Geller ◽  
Gabriel Rocha de Almeida Cunha
Keyword(s):  
Economic Development ◽  
Energy Consumption ◽  
Linear Regression ◽  
Energy Demand ◽  
Income Elasticity ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Linear Regression Models ◽  
Economic Sectors ◽  
The World

The causal relationship between energy demand and GDP has been the subject of intense research over the past three decades. The present work seeks to analyze the energy consumption evolution of different countries of the world and the relation of the same with the level of economic development, represented by income. For this purpose, an annual database containing gross domestic product and electricity consumption of 143 countries in the period between 1990 and 2014 was prepared. Thus, linear regression models and autoregressive vector models were used to explain the electricity consumption of countries and groups of countries. The results indicated that there is no standard elasticity behavior for most countries with similar levels of economic development. Despite this, a good performance was observed in the simple linear regression for aggregate data in groups of countries, which indicates the possibility of performing a reliable aggregate planning. Keywords: Electricity Demand, Energy Consumption, Income, GDP, Elasticity.

scholarly journals A Stochastic Estimation Framework for Yearly Evolution of Worldwide Electricity Consumption

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 256-266
Author(s):  
Qasem Abu Al-Haija
Keyword(s):  
Energy Demand ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Ar Model ◽  
Electric Energy Consumption ◽  
Electricity Use ◽  
Prediction Scheme ◽  
The World ◽  
Electrical Engineers ◽  
Regression Error

The determination of electric energy consumption is remarked as one of the most vital objectives for electrical engineers as it is highly essential in determining the actual energy demand made on the existing electricity supply. Therefore, it is important to find out about the increasing trend in electric energy demands and use all over the world. In this work, we present a prediction scheme for the progression of worldwide aggregates of cumulative electricity consumption using the time series of the records released annually for the net electricity use throughout the world. Consequently, we make use of an autoregressive (AR) model by retaining the best possible autoregression order recording the highest regression accuracy and the lowest standardized regression error. The resultant regression scheme was proficiently employed to regress and forecast the evolution of next-decade data for the net consumption of electricity worldwide from 1980 to 2019 (in billion kilowatt-hours). The experimental outcomes exhibited that the highest accuracy in regressing and forecasting the global consumption of electricity is 95.7%. The prediction results disclose a linearly growing trend in the amount of electricity issued annually over the past four decades’ observation for the global net electricity consumption dataset.

scholarly journals Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4046 ◽  
Author(s):  
Sooyoun Cho ◽  
Jeehang Lee ◽  
Jumi Baek ◽  
Gi-Seok Kim ◽  
Seung-Bok Leigh
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Simulation Analysis ◽  
Residential Buildings ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Building Energy ◽  
Major Effect ◽  
Measuring Instruments ◽  
Building Energy Consumption

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

Study on Dynamic Relationship between Economic Growth and Energy Consumption in Chongqing - Based on 1980-2011 Data

2013 ◽  
Vol 448-453 ◽  
pp. 4319-4324
Author(s):  
Sheng Wang ◽  
Chun Yan Dai ◽  
En Chuang Wang ◽  
Chun Yan Li
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Energy Consumption ◽  
Energy Demand ◽  
Industrial Structure ◽  
Impulse Response Function ◽  
Improve Energy Efficiency ◽  
Dynamic Relationship ◽  
Equilibrium Relationship

Analyzed the dynamic interaction characteristics of Chongqing Economic growth and energy consumption between 1980-2011 based on vector auto regression model, impulse response function. The results showed that: 1 Between the Chongqing's economic growth and energy consumption exist the positive long-term stable equilibrium relationship, Chongqing's economic development depending on energy consumption is too high, to keep the economy in Chongqing's rapid economic development, energy relatively insufficient supply sustainable development must rely on the energy market, which will restrict the development of Chongqing's economy. 2At this stage, Chongqing continuing emphasis on optimizing the industrial structure to improve energy efficiency at the same time, the key is to establish and improve the energy consumption intensity and total energy demand "dual control" under the security system, weakening the energy bottleneck effect on economic growth.

scholarly journals Analysis of Energy System in Norway with Focus on Energy Consumption Prediction

2017 ◽  
Vol 9 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Maryam Hamlehdar ◽  
Alireza Aslani
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Electricity Consumption ◽  
Energy System ◽  
High Energy ◽  
Regression Result ◽  
Industry Growth ◽  
The Status

Abstract Today, the fossil fuels have dominant share of energy supply in order to respond to the high energy demand in the world. Norway is one of the countries with rich sources of fossil fuels and renewable energy sources. The current work is to investigate on the status of energy demand in Norway. First, energy and electricity consumption in various sectors, including industrial, residential are calculated. Then, energy demand in Norway is forecasted by using available tools. After that, the relationship between energy consumption in Norway with Basic economics parameters such as GDP, population and industry growth rate has determined by using linear regression model. Finally, the regression result shows a low correlation between variables.

Energy demand projections and relevance of income dynamics in Gauteng’s residential sector

2011 ◽  
Vol 22 (4) ◽  
pp. 31-47 ◽  
Author(s):  
Mamahloko Senatla
Keyword(s):  
Energy Consumption ◽  
Low Income ◽  
Energy Demand ◽  
Electricity Consumption ◽  
Planning System ◽  
Strategy Development ◽  
Computing Power ◽  
Residential Sector ◽  
Energy Modelling ◽  
Main Driver

Energy modelling serves as a crucial tool for informing both energy policy and strategy development. But the modelling process is faced with both sectoral energy data and structural challenges. Among all the sectors, the residential sector usually presents a huge challenge to the modelling profession due to the dynamic nature of the sector. The challenge is brought by the fact that each an every household in a region may have different energy consumption characteristics and the computing power of the available models cannot incorporate all the details of individual household characteristics. Even if there was enough computing power within the models, energy consumption is collected through surveys and as a result only a sample of a region is captured. These challenges have forced energy modellers to categorise households that have similar characteristics. Different researchers choose different methods for categorising the households. Some researchers choose to categorise households by location and climate, others choose housing types while others choose quintiles. Currently, there is no consensus on which categorisation method takes precedence over others. In these myriad ways of categorising households, the determining factor employed in each method is what is assumed to be the driver of energy demand in that particular area of study. Many researchers acknowledge that households’ income, preferences and access to certain fuels determine how households use energy. Although many researchers recognise that income is the main driver of energy demand in the residential sector, there has been no energy modelling study that has tried to categorise households by income in South Africa. This paper chose to categorise households by income because income is taken to be the main driver of energy demand in the urban residential sector. Gauteng province was chosen as a case study area for this paper. The Long-range Energy Alternatives Planning System (LEAP) is used as a tool for such analysis. This paper will further reveal how the dynamics of differing income across the residential sector affects total energy demand in the long run. The households in Gauteng are classified into three income categories – high, middle and low income households. In addition to different income categories, the paper further investigates the energy demand of Gauteng’s residential sector under three economic scenarios with five energy demand scenarios. The three economic scenarios are first economic scenario (ECO1), second economic scenario (ECO2) and third economic scenario (ECO3). The most distinguishing factor between these economic scenarios is the mobility of households from one income band to the next.The model results show that electricity demand will be high in all the three economic scenarios. The reason for such high electrical energy demand in all the economic scenarios compared to other fuels is due to the fact that among all the provinces, Gauteng households have one of the highest electricity consumption profiles. ECO2 showed the highest energy demand in all the five energy demand scenarios. This is due to the fact that the share of high income households in ECO2 was very high, compared to the other two economic scenarios. The favourable energy demand scenarios will be the Energy Efficiency and MEPS scenarios due to their ability to reduce more energy demand than other scenarios in all the three economic scenarios.

scholarly journals Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

2021 ◽  
Author(s):  
Diego P. Pinto-Roa ◽  
Hernán Medina ◽  
Federico Román ◽  
Miguel García-Torres ◽  
Federico Divina ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Search Space ◽  
Consumption Pattern ◽  
Electric Energy Consumption ◽  
System A ◽  
Time Period ◽  
Computation Scheme ◽  
Significant Patterns

The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.

scholarly journals Electric energy consumption of milking process on some Finnish dairy farms

2017 ◽  
Vol 26 (3) ◽  
Author(s):  
Mari Rajaniemi ◽  
Tapani Jokiniemi ◽  
Laura Alakukku ◽  
Jukka Ahokas
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Electric Energy ◽  
Cooling Water ◽  
Electricity Consumption ◽  
Dairy Farms ◽  
Energy Costs ◽  
Heating Systems ◽  
Electric Energy Consumption ◽  
Energy Saving Potential

The aim of this study was to examine the electric energy consumption of milking process on dairy farms and to evaluate the methods to improve the energy efficiency. The electricity consumption of the milking process was measured on three dairy farms in Southern Finland, and it varied between 37–62 Wh kg-1 milk.  The largest energy saving potential was identified in milk cooling and the heating of cleaning water. Even simple methods, such as placing the condenser of the refrigeration system outside, may reduce the energy consumption of milk cooling by 30%. Efficient milk pre-cooling can reduce the energy consumption of the whole milking process by more than 25%. Even larger energy savings are possible with a sophisticated milk cooling – water heating systems. It was concluded that there is a significant potential to reduce the energy consumption and energy costs of the milking process, and thus to improve the profitability and sustainability of the sector at the same time.

scholarly journals Solar PV for Water Pumping and Irrigation

2015 ◽  
Vol 11 (1) ◽  
pp. 9-28
Author(s):  
I. Patay ◽  
M. Montvajszki
Keyword(s):  
Solar Energy ◽  
Energy Demand ◽  
Electric Energy ◽  
High Energy ◽  
Energy Costs ◽  
Solar Pv ◽  
Irrigation Period ◽  
Water Pumping ◽  
Pump Station ◽  
The World

Water pumping for irrigation has a relatively high energy demand, depending on the applied irrigation method. At the same time, there is a considerable energy from the sun during the irrigation period. The solar PV (photovoltaic) technology may be suitable to ensure electric energy for pumping in many cases in agriculture, where the electric network is not available or reduction of the energy costs is wanted. There are some pilot plants for water pumping on the base of solar energy in the world and the spreading of these solar technologies is predictable. The solar energy based pumping process can be approached both in theoretical and experimental ways. In this paper, both the theoretical questions of the solar based pumping process and the experimental results of a model testing pump station powered by PV panels are shown.

scholarly journals Smart Energy Consumption Could Preserve Natural Resources

ICR Journal ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 420-422
Author(s):  
Shahino Mah Abdullah
Keyword(s):  
Developing Countries ◽  
Energy Consumption ◽  
Natural Resources ◽  
Energy Demand ◽  
Cold Weather ◽  
World Population ◽  
Standards Of Living ◽  
The World ◽  
Increase Productivity ◽  
United Nation

Energy plays an important role in our lives. It comes in several forms which can be utilised to keep people warm during cold weather, provide foods, improve transportation, and increase productivity. When energy is utilised efficiently, it brings great comfort to our lives. However, energy consumption has been increasing in recent decades as the world population keeps growing. According to a United Nation (UN) report, the current world population of 7.4 billion is projected to increase by 1 billion over the next 10 years and reach 9.6 billion by 2050. Besides population, the standards of living for many people in developing countries is increasing, which in turn results in growing energy demand.

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