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Journal ArticleDOI

A review of energy models

S. Jebaraj, +1 more
- 01 Aug 2006 - 
- Vol. 10, Iss: 4, pp 281-311
TLDR
In this paper, a review paper on energy modeling will help the energy planners, researchers and policy makers widely, and an attempt has been made to understand and review the various emerging issues related to the energy modeling.
Abstract
Energy is a vital input for social and economic development of any nation. With increasing agricultural and industrial activities in the country, the demand for energy is also increasing. Formulation of an energy model will help in the proper allocation of widely available renewable energy sources such as solar, wind, bioenergy and small hydropower in meeting the future energy demand in India. During the last decade several new concepts of energy planning and management such as decentralized planning, energy conservation through improved technologies, waste recycling, integrated energy planning, introduction of renewable energy sources and energy forecasting have emerged. In this paper an attempt has been made to understand and review the various emerging issues related to the energy modeling. The different types of models such as energy planning models, energy supply–demand models, forecasting models, renewable energy models, emission reduction models, optimization models have been reviewed and presented. Also, models based on neural network and fuzzy theory have been reviewed and discussed. The review paper on energy modeling will help the energy planners, researchers and policy makers widely.

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Citations
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Towards a low-carbon economy: scenarios and policies for the UK

TL;DR: In this paper, the implications of long-term low-carbon scenarios for the UK, and against these it assesses both the current status and the required scope of the UK energy policy.
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Reinventing the energy modelling–policy interface

TL;DR: In this article, the authors propose a reinvention of the modelling-policy interface to provide timely, targeted, tested, transparent and iterated insights from such complex multidisciplinary tools.
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Raising awareness in model-based energy scenario studies—a transparency checklist

TL;DR: It is argued that transparency is a necessary condition for a reproducible and credible scenario study and the proposed transparency checklist represents a synthesizing tool for improving their transparency.
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Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States

TL;DR: In this paper, two types of numerical energy models were developed to predict the United States' future industrial energy demand. And they used an ANN (artificial neural network) technique and a MLR (multiple linear regression) technique.
References
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Journal ArticleDOI

Applications of artificial neural-networks for energy systems

TL;DR: In this paper, the authors present various applications of neural networks in energy problems in a thematic rather than a chronological or any other way, including modeling and design of a solar steam generating plant, estimation of a parabolic-trough collector's intercept factor and local concentration ratio, and performance prediction of solar water-heating systems.
Journal ArticleDOI

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TL;DR: In this paper, a multilayer recurrent architecture using the standard back-propagation learning algorithm has been applied to predict the energy consumption of a passive solar building for two seasons: winter and summer.
Journal ArticleDOI

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TL;DR: The authors show an application of the multicriteria decision-making methodology used to assess an action plan for the diffusion of renewable energy technologies at regional scale, carried out for Sardinia.
Journal ArticleDOI

Solar radiation model

TL;DR: In this article, the authors used the Angstrom-Prescott equation to predict the average daily global radiation with hours of sunshine for Hong Kong (22.3°N latitude, 114. 3°E longitude).
Journal ArticleDOI

A comparison of various forecasting techniques applied to mean hourly wind speed time series

TL;DR: A comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data, including the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks is presented.
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