Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

Notton, Gilles; Voyant, Cyril; Fouilloy, Alexis; Duchaud, Jean Laurent; Nivet, Marie Laure

doi:10.3390/app9010209

Cited by 85 publications

(39 citation statements)

References 51 publications

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“…For the optimal management of energy, the development of forecasting tools is needed to anticipate the rates of energy consumption. Since global horizontal irradiation data are rarely measured, Notton et al [16] built an artificial neural network model to estimate the values. As solar collectors are often tilted to face the sun, a second ANN model was further developed to transform horizontal irradiation data into global tilted irradiation data.…”

Section: Solar Power Cells and Energy Storagementioning

confidence: 99%

A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis

et al. 2019

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Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade (January 2008 to June 2018). First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. Non-supervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify sub-technology clusters and their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain. By adopting unsupervised learning approaches for literature and patent mining, this research develops a novel technology e-discovery methodology and presents the detailed reviews and analyses of the solar power technology using the proposed e-discovery workflow. The insights of global solar technology development, based on both comprehensive literature and patent reviews and cross-analyses, helps energy companies select advanced technologies related to their key technical R&D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development.

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Section: Solar Power Cells and Energy Storagementioning

confidence: 99%

A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis

et al. 2019

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“…The first paper [4], is about the use of ANNs to estimate and forecast solar radiation. Global horizontal irradiation (GHI) data are rarely measured worldwide, thus an artificial neural network (ANN) model is built to calculate this data based on available data.…”

Section: Artificial Neural Network For Energy Systemsmentioning

confidence: 99%

Special Issue on Applications of Artificial Neural Networks for Energy Systems

Tina

2019

Applied Sciences

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“…and, on the contrary, how to pre-and postprocess data in relation to the chosen ANN? e discussion on neural network structural aspects, such as the optimization of weights, the number of inputs, the choice of learning parameters, the number of layers, the number of neurons, activation functions, and inclusion of the statistical approach, has been vigorously ongoing for recent decades, e.g., [21][22][23][24][25]. Many researchers agree, however, that either there exists no universal and explicit method for tuning these parameters or there is rather few guidance in this matter [23,26,27].…”

Section: Introductionmentioning

confidence: 99%

ANN Architecture Specifications for Modelling of Open-Cell Aluminum under Compression

Dudzik

Stręk

2020

Mathematical Problems in Engineering

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e knowledge on strength properties of porous metals in compression is essential in tailored application design, as well as in elaboration of general material models. In this article, the authors propose specification details of the ANN architecture for adequate modelling of the phenomenon of compressive behaviour of open-cell aluminum. In the presented research, an algorithm was used to build different structures of artificial neural networks (ANNs), which approximated stress-strain relations of an aluminum sponge subjected to compression. Next, the quality of the built approximations was appraised. e mean absolute relative error (MARE), coefficient of determination between outputs and targets (R 2 ), root mean square error (RMSE), and mean square error (MSE) were assumed as criterial measures for the assessment of the fitting quality. e studied neural networks (NNs) were two-layer feedforward networks with different numbers of neurons in the hidden layer. A set of experimental stress-strain data from quasistatic uniaxial compression tests of open-cell aluminum of various apparent densities was used as data for training of neural networks. Analysis was performed in two modes: in the first one, all samples were taken for training, and in the second case, one sample was left out during training in order to play the role of external data for testing the trained network later. e taken out samples were maximum and minimum density samples (for extrapolation) and one random from within the density interval. e results showed that good approximation on the engineering level (MARE < 5%) was reached for teaching networks with ≥7 neurons in the hidden layer for the first studied case and with ≥8 neurons for the second. Calculations on external data proved that 8 neurons are enough to actually obtain MARE < 10%. Moreover, it was shown that the quality of approximation can be significantly improved to MARE ≈ 7% (tested on external data) if the initial region of the stress-strain relation is modelled by an additional network.

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Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

Cited by 85 publications

References 51 publications

A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis

A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis

Special Issue on Applications of Artificial Neural Networks for Energy Systems

ANN Architecture Specifications for Modelling of Open-Cell Aluminum under Compression

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