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

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.

An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation

“…Depending on data availability and objectives of the analysis and time horizon, a different approach needs to be considered among quantitative forecasting techniques such as physical methods, statistical methods and advanced methods which are based on the computational intelligence techniques [31]. In [32], the artificial neural networks and the support vector machine techniques were proposed for energy generation forecasting and condition-based maintenance strategies in PV plants. For planning and controlling electricity load, short-term forecasting ranging from hours to days is effective and for a national or regional scope of operations and the impact of policy in longer-term, long-term forecasting is utilized [33].…”

Ensemble of Regression-Type and Interpolation-Type Metamodels

“…First, β calculated by Equation (9) can be used to substitute the one in Equation (6). Second, the approximate responses of established PRS (ŷ 1 1,prs ,ŷ 2 1,prs , .…”

“…. , x n ) can be calculated according to Equation (6). Then, the updated training dataset of PRS can be expressed as…”

“…Westermann and Evins [5] summarized and discussed recent studies on the application of metamodels in sustainable building design. Ferrero Bermejo et al [6] reviewed and compared two typical metamodels, namely the artificial neural networks and the support vector machine, for energy forecasting and condition-based maintenance in PV plants.…”

Trusted Simulation Using Proteus Model for a PV System: Test Case of an Improved HC MPPT Algorithm

“…This development has pushed many researchers to search for important solutions to increase the PV energy extracted from the PV panels. Among these solutions, we find MPPT techniques [4][5][6][7]. These techniques are used to control a DC-DC converter to extract the maximum power from the PV system.…”