A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

被引:117
|
作者
Ferrero Bermejo, Jesus [1 ]
Gomez Fernandez, Juan F. [2 ]
Olivencia Polo, Fernando [1 ]
Crespo Marquez, Adolfo [2 ]
机构
[1] Magtel Operac, Seville 41309, Spain
[2] Escuela Tecn Super Ingn, Dept Ind Management, Seville 41092, Spain
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 09期
基金
欧盟地平线“2020”;
关键词
renewable energy; artificial neural network; artificial intelligence; survey; CONDITION-BASED MAINTENANCE; GENERATION; SYSTEMS; ANN; OPTIMIZATION; TEMPERATURE; MANAGEMENT; ALGORITHM; FORECASTS; STORAGE;
D O I
10.3390/app9091844
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.
引用
收藏
页数:20
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