Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States

In the United States, the industrial sector is the driving engine of economic development, and energy consumption in this sector may be considered as the fuel for this engine. In order to keep this sector sustainable (diverse and productive over the time), energy planning should be carried out comprehensively and precisely. This paper describes the development of two types of numerical energy models which are able to predict the United States' future industrial energy-demand. One model uses an ANN (artificial neural network) technique, and the other model uses a MLR (multiple linear regression) technique. Various independent variables (GDP, price of energy carriers) are tested. The future industrial energy demand can then be forecasted based on a defined scenario.

[1]  Ramazan Sari,et al.  The relationship between disaggregate energy consumption and industrial production in the United States: An ARDL approach , 2008 .

[2]  Ismael Herrera,et al.  Mathematical Modeling in Science and Engineering: An Axiomatic Approach , 2012 .

[3]  Xiping Wang,et al.  Grey prediction with rolling mechanism for electricity demand forecasting of Shanghai , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[4]  G. Boyd,et al.  Modeling of industrial energy consumption: An introduction and context , 2007 .

[5]  Hadi Dowlatabadi,et al.  US long-term energy intensity: Backcast and projection , 2006 .

[6]  J. Fisher,et al.  Is there a water-energy nexus in electricity generation? Long-term scenarios for the western United States , 2013 .

[7]  Mohammad Ghodsi,et al.  Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data , 2005, BMC Medical Informatics Decis. Mak..

[8]  Ramazan Sari,et al.  Disaggregate energy consumption and industrial output in the United States , 2007 .

[9]  Ali Azadeh,et al.  An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran , 2010 .

[10]  James E. Payne,et al.  On the dynamics of energy consumption and output in the US , 2009 .

[11]  Patrik Söderholm,et al.  Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models , 2006 .

[12]  Alper Ünler,et al.  Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025 , 2008 .

[13]  Diyar Akay,et al.  Grey prediction with rolling mechanism for electricity demand forecasting of Turkey , 2007 .

[14]  John P. Weyant,et al.  End use technology choice in the National Energy Modeling System (NEMS): An analysis of the residential and commercial building sectors , 2013 .

[15]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[16]  Maximilian Auffhammer,et al.  The Rationality of EIA Forecasts under Symmetric and Asymmetric Loss , 2005 .

[17]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[18]  Robert U. Ayres,et al.  Evidence of causality between the quantity and quality of energy consumption and economic growth , 2010 .

[19]  Fatih Karanfil,et al.  How many times again will we examine the energy-income nexus using a limited range of traditional econometric tools? , 2009 .

[20]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[21]  S. Agatonovic-Kustrin,et al.  Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. , 2000, Journal of pharmaceutical and biomedical analysis.

[22]  E. Worrell,et al.  Barriers to energy efficiency in industrial bottom-up energy demand models—A review , 2011 .

[23]  A. Kialashaki,et al.  Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks , 2013 .

[24]  A. Osmani,et al.  Electricity generation from renewables in the United States: Resource potential, current usage, technical status, challenges, strategies, policies, and future directions , 2013 .

[25]  James J. Winebrake,et al.  An evaluation of errors in US energy forecasts: 1982–2003 , 2006 .

[26]  David I. Stern,et al.  Energy and economic growth in the USA: A multivariate approach , 1993 .

[27]  Nicholas Apergis,et al.  Energy consumption and economic growth in Central America: Evidence from a panel cointegration and error correction model , 2009 .

[28]  M. Toksari.  Ant colony optimization approach to estimate energy demand of Turkey , 2007 .

[29]  Martin T. Hagan,et al.  Neural network design , 1995 .

[30]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[31]  William E. Roper,et al.  Energy demand estimation of South Korea using artificial neural network , 2009 .

[32]  C. Hamzaçebi.  Forecasting of Turkey's net electricity energy consumption on sectoral bases , 2007 .

[33]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[34]  Ali Azadeh,et al.  Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors , 2008 .

[35]  D. Stern,et al.  Aggregation and the role of energy in the economy , 2000 .

[36]  Mehmet Bilgili,et al.  Electric energy demands of Turkey in residential and industrial sectors , 2012 .

[37]  K. Simpson,et al.  An artificial neural network can select patients at high risk of developing progressive IgA nephropathy more accurately than experienced nephrologists. , 1998, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[38]  M. Qi,et al.  Forecasting Aggregate Retail Sales: a Comparison of Arti"cial Neural Networks and Traditional Methods , 2001 .

[39]  Amit Kanudia,et al.  Advanced bottom-up modelling for national and regional energy planning in response to climate change , 1997 .

[40]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[41]  Pierre-André Haldi,et al.  Dynamic formulation of a top-down and bottom-up merging energy policy model , 2003 .

[42]  Saleh M. Al-Alawi,et al.  Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations , 2005, Environ. Model. Softw..

[43]  David I. Stern,et al.  A multivariate cointegration analysis of the role of energy in the US macroeconomy , 2000 .

[44]  B. Ewing,et al.  Energy consumption, income, and carbon emissions in the United States , 2007 .

[45]  S. Iniyan,et al.  A review of energy models , 2006 .

[46]  John Asafu-Adjaye,et al.  The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries , 2000 .

[47]  James E. Payne,et al.  Sectoral Analysis of the Causal Relationship Between Renewable and Non-Renewable Energy Consumption and Real Output in the US , 2010 .

[48]  Ali Azadeh,et al.  A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran , 2008 .

[49]  Yochanan Shachmurove,et al.  Modeling and forecasting energy consumption in China: Implications for Chinese energy demand and imports in 2020 , 2008 .

[50]  Serhat Kucukali,et al.  Turkey’s short-term gross annual electricity demand forecast by fuzzy logic approach , 2010 .

[51]  Bruce Tonn,et al.  Power from Perspective: Potential future United States energy portfolios , 2009 .

[52]  Christoph Böhringer,et al.  The synthesis of bottom-up and top-down in energy policy modeling , 1998 .