Journal ArticleDOI
Artificial neural networks in renewable energy systems applications: a review
Reads0
Chats0
TLDR
In this article, the authors present various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use.Abstract:
Artificial neural networks are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and, once trained, can perform prediction and generalisation at high speed. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social/psychological sciences. They are particularly useful in system modelling such as in implementing complex mappings and system identification. This paper presents various applications of neural networks mainly in renewable energy problems in a thematic rather than a chronological or any other order. Artificial neural networks have been used by the author in the field of solar energy; for modelling and design of a solar steam generating plant, for the estimation of a parabolic trough collector intercept factor and local concentration ratio and for the modelling and performance prediction of solar water heating systems. They have also been used for the estimation of heating loads of buildings, for the prediction of air flow in a naturally ventilated test room and for the prediction of the energy consumption of a passive solar building. In all those models a multiple hidden layer architecture has been used. Errors reported in these models are well within acceptable limits, which clearly suggest that artificial neural networks can be used for modelling in other fields of renewable energy production and use. The work of other researchers in the field of renewable energy and other energy systems is also reported. This includes the use of artificial neural networks in solar radiation and wind speed prediction, photovoltaic systems, building services systems and load forecasting and prediction.read more
Citations
More filters
Journal ArticleDOI
Performance evaluation of air ejectors using artificial neural network approach
Journal Article
Design and Control of PEM Fuel Cell Diffused Aeration System using Artificial Intelligence Techniques
TL;DR: In this paper, the authors presented a new design and control of PEM fuel cell powered a diffused air aeration system for a shrimp farm in Mersa Matruh in Egypt.
Proceedings ArticleDOI
Adaptive Ultra-Short-Term Rolling Forecast Model of Photovoltaic Power
TL;DR: Wang et al. as mentioned in this paper proposed an adaptive ultra-short-term rolling prediction method to improve the accuracy of the prediction model according to historical photovoltaic power, which can not only provide the reference for grid dispatching, but also improve the safety and stability of the distribution network.
Zastosowanie metod sztucznej inteligencji w energetyce
TL;DR: The authors prezentuje możliwości wykorzystania metod sztucznej inteligencji w energetyce.
Proceedings ArticleDOI
A Neural Network Approach for short-term forecasting of PV Generation in Dwellings
TL;DR: In this paper, the preliminary results of an undergoing research regarding the 24-hour prediction of a PV production, in a dwelling in Cyprus, were presented, showing an overall correlation of 99% approx. and a Mean Squared Error of 1.4% approx for a cloudy day.
References
More filters
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.
Related Papers (5)
Artificial intelligence techniques for photovoltaic applications: A review
Adel Mellit,Soteris A. Kalogirou +1 more