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Jesús Ferrero Bermejo
Magtel Operaciones, 41940 Seville, Spain

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Review
Published: 31 October 2019 in Energies
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Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important effort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the different outputs for the different techniques.

ACS Style

Jesús Ferrero Bermejo; Juan Francisco Gómez Fernández; Rafael Pino; Adolfo Crespo Márquez; Antonio Jesús Guillén López. Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants. Energies 2019, 12, 4163 .

AMA Style

Jesús Ferrero Bermejo, Juan Francisco Gómez Fernández, Rafael Pino, Adolfo Crespo Márquez, Antonio Jesús Guillén López. Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants. Energies. 2019; 12 (21):4163.

Chicago/Turabian Style

Jesús Ferrero Bermejo; Juan Francisco Gómez Fernández; Rafael Pino; Adolfo Crespo Márquez; Antonio Jesús Guillén López. 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants." Energies 12, no. 21: 4163.

Review
Published: 05 May 2019 in Applied Sciences
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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.

ACS Style

Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Fernando Olivencia Polo; Adolfo Crespo Márquez. 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. Applied Sciences 2019, 9, 1844 .

AMA Style

Jesús Ferrero Bermejo, Juan F. Gómez Fernández, Fernando Olivencia Polo, Adolfo Crespo Márquez. 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. Applied Sciences. 2019; 9 (9):1844.

Chicago/Turabian Style

Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Fernando Olivencia Polo; Adolfo Crespo Márquez. 2019. "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." Applied Sciences 9, no. 9: 1844.

Journal article
Published: 01 April 2015 in Renewable Energy
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In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities.

ACS Style

Fernando A. Olivencia Polo; Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Adolfo Crespo Márquez. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy 2015, 81, 227 -238.

AMA Style

Fernando A. Olivencia Polo, Jesús Ferrero Bermejo, Juan F. Gómez Fernández, Adolfo Crespo Márquez. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy. 2015; 81 ():227-238.

Chicago/Turabian Style

Fernando A. Olivencia Polo; Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Adolfo Crespo Márquez. 2015. "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models." Renewable Energy 81, no. : 227-238.