Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/21/4163 |
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| author | Jesús Ferrero Bermejo Juan Francisco Gómez Fernández Rafael Pino Adolfo Crespo Márquez Antonio Jesús Guillén López |
| author_facet | Jesús Ferrero Bermejo Juan Francisco Gómez Fernández Rafael Pino Adolfo Crespo Márquez Antonio Jesús Guillén López |
| author_sort | Jesús Ferrero Bermejo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:44:35Z |
| format | Article |
| id | doaj.art-0f23b696f2b5452eb2985f9bc0a8d6c5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:44:35Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0f23b696f2b5452eb2985f9bc0a8d6c52022-12-22T04:01:28ZengMDPI AGEnergies1996-10732019-10-011221416310.3390/en12214163en12214163Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV PlantsJesús Ferrero Bermejo0Juan Francisco Gómez Fernández1Rafael Pino2Adolfo Crespo Márquez3Antonio Jesús Guillén López4Magtel Operaciones, 41940 Seville, SpainDepartment of Industrial Management, Escuela Técnica Superior de Ingenieros, 41092 Sevilla, SpainDepartment of Statistics and Operations Research, Facultad de Matemáticas, Universidad de Sevilla, 41012 Sevilla, SpainDepartment of Industrial Management, Escuela Técnica Superior de Ingenieros, 41092 Sevilla, SpainDepartment of Industrial Management, Escuela Técnica Superior de Ingenieros, 41092 Sevilla, SpainWithin 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.https://www.mdpi.com/1996-1073/12/21/4163artificial intelligence techniquesenergy forecastingcondition-based maintenanceasset management |
| spellingShingle | 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 artificial intelligence techniques energy forecasting condition-based maintenance asset management |
| title | Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants |
| title_full | Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants |
| title_fullStr | Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants |
| title_full_unstemmed | Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants |
| title_short | Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants |
| title_sort | review and comparison of intelligent optimization modelling techniques for energy forecasting and condition based maintenance in pv plants |
| topic | artificial intelligence techniques energy forecasting condition-based maintenance asset management |
| url | https://www.mdpi.com/1996-1073/12/21/4163 |
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