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.
Highlights
Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence [1] pretending to generate new business data knowledge transforming sets of "raw data" into business value
The recent consolidation of PHM as an engineering discipline, including the application of analytical techniques, such as data mining techniques, has promoted a new condition-based maintenance (CBM) by providing new capabilities and unprecedented potential to understand and obtain useful information on the deterioration of systems and their behaviour patterns over their lifetime [49,50,51], deepening more effective and adaptable solutions according to changes [52]
Each tree is grown by binary recursive partitioning, where each split is determined by a search procedure aimed to find the variable of a partition rule which provides the maximum reduction in the sum of the squared error
Summary
Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence [1] pretending to generate new business data knowledge transforming sets of "raw data" into business value. In order to take in rapid optimal decisions,DM the techniques, challenge is comparing to structuretheir the results whenfrom applied to a similar casesynchronizing study This issue is often not addressed when applying information different sources, it properly in time, in a sustainable andcertain complex intelligent optimization techniques, and no discussion concerning this assimilable way, reducing the modelling errors This is because, often, real the computational to applyframework a certain method verypermanent important interferences) and valuing risks. A case study in a photovoltaic plant
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