Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Jesús Ferrero Bermejo, Juan Francisco Gómez Fernández, Rafael Pino, Adolfo Crespo Márquez and Antonio Jesús Guillén López
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Jesús Ferrero Bermejo: Magtel Operaciones, 41940 Seville, Spain
Juan Francisco Gómez Fernández: Department of Industrial Management, Escuela Técnica Superior de Ingenieros, 41092 Sevilla, Spain
Rafael Pino: Department of Statistics and Operations Research, Facultad de Matemáticas, Universidad de Sevilla, 41012 Sevilla, Spain
Adolfo Crespo Márquez: Department of Industrial Management, Escuela Técnica Superior de Ingenieros, 41092 Sevilla, Spain
Antonio Jesús Guillén López: Department of Industrial Management, Escuela Técnica Superior de Ingenieros, 41092 Sevilla, Spain

Energies, 2019, vol. 12, issue 21, 1-18

Abstract: 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.

Keywords: artificial intelligence techniques; energy forecasting; condition-based maintenance; asset management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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