Transport Energy Demand Modeling of the United States Using Artificial Neural Networks and Multiple Linear Regressions

2014 ◽  
Author(s):  
Arash Kialashaki ◽  
John Reisel
Keyword(s):  
Neural Network ◽  
United States ◽  
Energy Demand ◽  
Regression Models ◽  
The United States ◽  
Transportation Energy ◽  
Independent Variables ◽  
Transportation Sector ◽  
The Future ◽  
Artificial Neural

In 2009, the transportation sector was the second largest consumer of primary energy in the United States, following the electric power sector and followed by the industrial, residential, and commercial sectors. The pattern of energy use varies by sector. For example, petroleum provides 96% of the energy used for transportation but its share is much less in other sectors. While the United States consumes vast quantities of energy, it has also pledged to cut its greenhouse gas emissions by 2050. In order to assist in planning for future energy needs, the purpose of this study is to develop a model for transport energy demand that incorporates past trends. This paper describes the development of two types of transportation energy models which are able to predict the United States’ future transportation energy-demand. One model uses an artificial neural network technique (a feed-forward multilayer perceptron neural network coupled with back-propagation technique), and the other model uses a multiple linear regression technique. Various independent variables (including GDP, population, oil price, and number of vehicles) are tested. The future transport energy demand can then be forecast based on the application of the growth rate of effective parameters on the models. The future trends of independent variables have been predicted based on the historical data from 1980 using a regression method. Using the forecast of independent variables, the energy demand has been forecasted for period of 2010 to 2030. In terms of the forecasts generated, the models show two different trends despite their performances being at the same level during the model-test period. Although, the results from the regression models show a uniform increase with different slopes corresponding to different models for energy demand in the near future, the results from ANN express no significant change in demand in same time frame. Increased sensitivity of the ANN models to the recent fluctuations caused by the economic recession may be the reason for the differences with the regression models which predict based on the total long-term trends. Although a small increase in the energy demand in the transportation sector of the United States has been predicted by the models, additional factors need to be considered regarding future energy policy. For example, the United States may choose to reduce energy consumption in order to reduce CO2 emissions and meet its national and international commitments, or large increases in fuel efficiency may reduce petroleum demand.

scholarly journals The United States’ Clothing Imports from Asian Countries along the Belt and Road: An Extended Gravity Trade Model with Application of Artificial Neural Network

2020 ◽  
Vol 12 (18) ◽  
pp. 7433
Author(s):  
Danny Chi Kuen Ho ◽  
Eve Man Hin Chan ◽  
Tsz Leung Yip ◽  
Chi-Wing Tsang
Keyword(s):  
Neural Network ◽  
United States ◽  
Artificial Neural Network ◽  
Developing Countries ◽  
Trade Flow ◽  
The United States ◽  
Extended Gravity ◽  
Belt And Road ◽  
Artificial Neural ◽  
The Belt And Road

In 2013, China announced the Belt and Road Initiative (BRI), which aims to promote the connectivity of Asia, Europe, and Africa and deepen mutually beneficial economic cooperation among member countries. Past studies have reported a positive impact of the BRI on trade between China and its partner countries along the Belt and Road (B&R). However, less is known about its effect on the sectoral trade between the B&R countries and countries that show little support of the BRI. To address that gap, this study examines the changing patterns of clothing imports by the United States (US) from China and 14 B&R countries in Asia. An extended gravity model with a policy variable BRI is built to explain bilateral clothing trade flow. A panel regression model and artificial neural network (ANN) are developed based on the data collected from 1998 to 2018 and applied to predict the trade pattern of 2019. The results show a positive effect of the BRI on the clothing exports of some Asian developing countries along the B&R to the US and demonstrate the superior predictive power of the ANN. More research is needed to examine the balance between economic growth and the social and environmental sustainability of developing countries and to apply more advanced machine learning algorithms to examine global trade flow under the BRI.

scholarly journals Energy Use Efficiency Past-to-Future Evaluation: An International Comparison

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3804 ◽  
Author(s):  
Chia-Nan Wang ◽  
Thi-Duong Nguyen ◽  
Min-Chun Yu
Keyword(s):  
United States ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Demand ◽  
Energy Use ◽  
The United States ◽  
Total Energy Consumption ◽  
The Future ◽  
The Status ◽  
Energy Efficiency Evaluation

Despite the many benefits that energy consumption brings to the economy, consuming energy also leads nations to expend more resources on environmental pollution. Therefore, energy efficiency has been proposed as a solution to improve national economic competitiveness and sustainability. However, the growth in energy demand is accelerating while policy efforts to boost energy efficiency are slowing. To solve this problem, the efficiency gains in countries where energy consumption efficiency is of the greatest concern such as China, India, the United States, and Europe, especially, emerging economies, is central. Additionally, governments must take greater policy actions. Therefore, this paper studied 25 countries from Asia, the Americas, and Europe to develop a method combining the grey method (GM) and data envelopment analysis (DEA) slack-based measure model (SMB) to measure and forecast the energy efficiency, so that detailed energy efficiency evaluation can be made from the past to the future; moreover, this method can be extended to more countries around the world. The results of this study reveal that European countries have a higher energy efficiency than countries in Americas (except the United States) and Asian countries. Our findings also show that an excess of total energy consumption is the main reason causing the energy inefficiency in most countries. This study contributes to policymaking and strategy makers by sharing the understanding of the status of energy efficiency and providing insights for the future.

scholarly journals Modeling of Building Energy Consumption by Integrating Regression Analysis and Artificial Neural Network with Data Classification

Buildings ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 198
Author(s):  
Iffat Ridwana ◽  
Nabil Nassif ◽  
Wonchang Choi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Demand ◽  
Regression Models ◽  
Mean Squared Error ◽  
Data Classification ◽  
Building Energy ◽  
Modern World ◽  
Artificial Neural ◽  
Conventional Regression

With the constant expansion of the building sector as a major energy consumer in the modern world, the significance of energy-efficient building systems cannot be more emphasized. Most of the buildings are now equipped with an electric dashboard to record consumption data which presents a significant scope of research by utilizing those data in energy modeling. This paper investigates conventional regression modeling in building energy estimation and proposes three models with data classifications to improve their performance. The proposed models are regression models and an artificial neural network model with data classification for predicting hourly or sub-hourly energy usage in four different buildings. Energy data is collected from a building energy simulation program and existing buildings to develop the models for detailed analysis. Data classification is recommended according to the system operating schedules of the buildings and models are tested for their performance in capturing the data trends resulting from those schedules. Proposed regression models and an ANN model with the recommended classification show very accurate results in estimating energy demand compared to conventional regression models. Correlation coefficient and root mean squared error values improve noticeably for the proposed models and they can potentially be utilized for energy conservation purposes and energy savings in the buildings.

Communicating about Biofuels and Climate Change

2017 ◽  
Author(s):  
Michael A. Cacciatore
Keyword(s):  
Climate Change ◽  
United States ◽  
Global Climate Change ◽  
Energy Demand ◽  
Alternative Energy ◽  
Global Climate ◽  
New Technology ◽  
The United States ◽  
Transportation Sector ◽  
Carbon Dioxide Co2

Biofuels are produced from biomass, which is any organic matter that can be burned or otherwise used to produce heat or energy. While not a new technology—biofuels have been around for well over 100 years—they are experiencing something of a renaissance in the United States and other countries across the globe. Today, biofuels have become the single most common alternative energy source in the U.S. transportation sector with billions of gallons of the fuel produced annually. The expansion of the bio-based economy in recent years has been intertwined with mounting concerns about environmental pollution and the accumulation of carbon dioxide (CO2) in the earth’s atmosphere. In the United States, for example, biofuels mandates have been championed as key to solving not only the country’s increasing energy demand problems and reliance on foreign oil, but also growing fears about global climate change. Of course, the use of biomass and biofuels to combat global climate change has been highly controversial. While proponents argue that biofuels burn cleaner than gasoline, research has suggested that any reductions in CO2 emissions are offset by land use considerations and the energy required in the biofuels-production process. How publics perceive of climate change as a problem and the use of biomass and biofuels as potential solutions will go a long way toward determining the policies that government’s implement to address this issue.

Sign in / Sign up

Export Citation Format

Share Document