Journal ArticleDOI
Applications of artificial neural-networks for energy systems
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TLDR
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.About:
This article is published in Applied Energy.The article was published on 2000-09-01. It has received 833 citations till now. The article focuses on the topics: Artificial neural network & Solar energy.read more
Citations
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Journal ArticleDOI
Solar forecasting methods for renewable energy integration
TL;DR: In this article, the authors review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.
Journal ArticleDOI
A review on applications of ANN and SVM for building electrical energy consumption forecasting
Ahmad Sukri Ahmad,Mohammad Yusri Hassan,M. P. Abdullah,Hasimah Abdul Rahman,Faridah Hussin,Huda Abdullah,Rahman Saidur +6 more
TL;DR: This paper reviews the building electrical energy forecasting method using artificial intelligence (AI) methods such as support vector machine (SVM) and artificial neural networks (ANN), regarding the potential of hybrid method of Group Method of Data Handling and Least Square Support Vector Machine (LSSVM), or known as GLSSVM, to forecastBuilding electrical energy consumption.
Journal ArticleDOI
A review of energy models
S. Jebaraj,S. Iniyan +1 more
TL;DR: 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.
Journal ArticleDOI
State of the art in building modelling and energy performances prediction: A review
TL;DR: A detailed review and discussion of these works can be found in this article, where the authors present the main machine learning tools used for prediction of energy consumption, heating/cooling demand, indoor temperature.
Journal ArticleDOI
A review on time series forecasting techniques for building energy consumption
TL;DR: The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building and the nine most popular forecasting techniques based on the machine learning platform are analyzed.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.