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      Authors - publish your SDGs-related research with EDP Sciences. Find out more.

      Authors - did you know EPJ Photovoltaics has been awarded the DOAJ Seal for “best practice in open access publishing”?

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      Long-term PV system modelling and degradation using neural networks

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          Abstract

          The power production of photovoltaic plants can be affected throughout its operational lifetime by multiple losses and degradation mechanisms. Although long-term degradation has been widely studied, most methodologies assume a specific degradation behaviour and require detailed metadata. This paper presents a methodology for the calculation of long-term degradation of a photovoltaic plant based on neural networks. The goal of the neural network is to model the photovoltaic plant's power production as a function of environmental conditions and time elapsed since the plant started operating. A big advantage of this method with respect to others is that it is completely data-driven, requires no additional information, and makes no assumptions related to degradation behaviour. Results show that the model can derive a long-term degradation trend without overfitting to shorter-term effects or abrupt changes in year-to-year operation.

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          Most cited references21

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          Popular Ensemble Methods: An Empirical Study

          An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
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            Deep Reinforcement Learning with Double Q-Learning

            The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.
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              Photovoltaic Degradation Rates-an Analytical Review

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                Author and article information

                Journal
                epjpv
                https://www.epj-pv.org
                EPJ Photovoltaics
                EPJ Photovolt.
                EDP Sciences
                2105-0716
                23 October 2023
                23 October 2023
                2023
                : 14
                : ( publisher-idID: epjpv/2023/01 )
                : 30
                Affiliations
                [1 ] GreenPowerMonitor a DNV company, Gran Via de les Corts Catalanes, 130, , Barcelona, Spain,
                [2 ] DNV Denmark, Tuborg Parkvej 8, , Hellerup, Denmark,
                Author notes
                Article
                pv230026
                10.1051/epjpv/2023018
                1ff0c447-ae03-4ebd-9d3f-17096b7001a8
                © G. Guerra et al., Published by EDP Sciences, 2023

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 June 2023
                : 17 August 2023
                : 25 August 2023
                Page count
                Figures: 7, Tables: 5, Equations: 23, References: 31, Pages: 10
                Categories
                Modelling
                Special Issue on ‘EU PVSEC 2023: State of the Art and Developments in Photovoltaics’, edited by Robert Kenny and João Serra
                Regular Article
                Custom metadata
                EPJ Photovoltaics 14, 30 (2023)
                yes
                2023
                2023
                2023

                Sustainable & Green chemistry,Materials technology,Semiconductors,Materials for energy,Technical & Applied physics,Renewable energy
                long-term degradation,automatic differentiation,neural networks,Photovoltaic generation,machine learning

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