Characterisation and Modeling of Organic Solar Cells by Using Radial Basis Neural Networks

Gotleyb, Dor; Sciuto, Grazia Lo; Napoli, Christian; Shikler, Rafi; Tramontana, Emiliano; Woźniak, Marcin

doi:10.1007/978-3-319-39378-0_9

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…Meanwhile, artificial intelligence techniques have become an excellent tool for wind generation and PV generation [15,16] and can solve the problem of a non-linear function estimation [17]. For example, the neural networks can be used to model the characteristics of traditional silicon-based PV modules; then, such networks, together with the intelligent algorithmic, are utilized to automatically parametrize the Voltage-Current characteristics of organic PV modules [18]. Meanwhile, the artificial intelligence technology is also playing a key role in the energy domain, such as the control system [19], energy consumption [20], pattern recognition and failure classification [21,22].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

et al. 2019

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The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.

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Section: Introductionmentioning

confidence: 99%

Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

et al. 2019

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“…Solar photovoltaic cells have experienced rapid development because they represent a key field in the cleaner electrical energy production. Those who grapple with solar photovoltaic science and technology devoted themselves to the study of novel materials and fabrication methods and device configurations for the next generation of solar cells, which must be efficient, low cost, lightweight and flexible [1].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, for the newly developed technology, namely the organic solar cells of last generation, it has important significance because they have very interesting properties and many advantages [1][2], including the cell flexibility and the ability to be exploited in large areas, the low costs of materials and organic cells fabrication [3][4]. However, the improvement of efficiency and stability must be considerably compared to their current state.…”

Section: Introductionmentioning

confidence: 99%

“…However, the improvement of efficiency and stability must be considerably compared to their current state. But it is very difficult to obtain an matimatical model starting from a priori assumptions, due to the complex nature of the material of OPV itself [1]. On the contrary, an independent model technique is paramount to automatically extract model parameters for the electrical characteristics of this new kinds of photovoltaic cells.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Ghaitaoui¹,

Benatiallah²,

Khachab³

et al. 2019

EJEE

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In this paper, the characterization and modeling results of the electrical parameters of the tendem organic photovoltaic cells (infinityPV) are presented. The electrical performances of this organic cell's module (I-V and P-V) are characterized and analyzed in the weather condition of southwest Algeria (Adrar site). A program in MATLAB language is developed locally for modeling the organic cell, using artificial intelligence. The multilayer feed forward perception type ANN is used for extraction of the organic photovoltaic model performances, and the back-propagation algorithm is used for the program training. The ANN obtained results by the simulation showed a good conformity with the results obtained experimentally in the saharan climate (MAPE:0.68%, MBE:4.5018e-09 A and RMSE:1.8062e-04 A).

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“…The theory of the NN operators has been widely studied in last decade in view of its relation with the well-known theory of the feed-forward artificial neural networks, see [1,[12][13][14]. Neural networks have been widely used in many applications fields: such as in artificial intelligence ( [11,[31][32][33]41]), neuro sciences and in approximation theory ( [3,35,40,42]).…”

Section: Introductionmentioning

confidence: 99%

Approximation Results in Orlicz Spaces for Sequences of Kantorovich Max-Product Neural Network Operators

Costarelli

Sambucini

2018

Results Math

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In this paper we study the theory of the so-called Kantorovich max-product neural network operators in the setting of Orlicz spaces L ϕ . The results here proved, extend those given by the authors in Result Math., 2016, to a more general context. The main advantage in studying neural network type operators in Orlicz spaces relies in the possibility to approximate not necessarily continuous functions (data) belonging to different function spaces by a unique general approach. Further, in order to derive quantitative estimates in this context, we introduce a suitable K-functional in L ϕ and use it to provide an upper bound for the approximation error of the above operators. Finally, examples of sigmoidal activation functions have been considered and studied.1991 Mathematics Subject Classification. Primary: 41A25, 41A05, 41A30, 47A58.

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Characterisation and Modeling of Organic Solar Cells by Using Radial Basis Neural Networks

Cited by 8 publications

References 23 publications

Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach

Neural Network Modeling and Experimental Evaluation of Organic Solar Panel Performance in Algerian Sahara

Approximation Results in Orlicz Spaces for Sequences of Kantorovich Max-Product Neural Network Operators

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