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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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Non-Smooth Dynamics in Energy Market Models: A Complex Approximation From System Dynamics and Dynamical Systems Approach

TL;DR: A general model that describes the supply and demand of electricity in a national market based on the system dynamics approach and extends its investigation results to any energy market model attached to various investment decisions, confirming the generalizability of the research.
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Incorporating risk in forest sector modeling – state of the art and promising paths for future research

TL;DR: For incorporating risk in FSM, fuzzy set theory and robust optimization techniques seem promising new approaches, alongside methods that already are in use, like Monte Carlo simulation and, in particular, scenario and sensitivity analysis.
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Qualitative Analysis of Household Energy Awareness in Poland

TL;DR: In this paper , a survey was conducted in Poland with over 1097 respondents and the results of the study clearly show that the respondents have knowledge of energy and the factors that influence the increase in its consumption but are not aware of why energy should be saved and what it means for the environment.
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G M (1, 1) Analysis and Forecasting for Efficient Energy Production and Consumption

TL;DR: Grey Model (1, 1) based on grey system has been used for forecasting results and outperformed compared with model fitting and model forecasting, and performance of the proposed technique has been compared with existing Auto regressive moving average forecasting model.
References
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Journal ArticleDOI

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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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Journal ArticleDOI

Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology

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Journal ArticleDOI

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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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