Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
|Author/s||Ferrero Bermejo, Jesús
Gómez Fernández, Juan Francisco
Pino Mejías, Rafael
Crespo Márquez, Adolfo
Guillén López, Antonio Jesús
|Department||Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
|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 ...
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 e ort 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 di erent outputs for the di erent techniques.
|Citation||Ferrero Bermejo, J., Gómez Fernández, J.F., Pino Mejías, R., Crespo Márquez, A. y Guillén López, A.J. (2019). Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants. Energies, 12 (21). Article number 4163.|