Selection of most relevant input parameters for predicting photovoltaic output power using machine learning and quadratic models

Kassem, Youssef; Othman, Adnan Alhaj

doi:10.1007/s40808-022-01413-7

Cited by 10 publications

(1 citation statement)

References 37 publications

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“…At first glance, using large-capacity energy storage devices, such as batteries, escalates the initial cost of deployment and negatively affects the utility grid's power quality. Secondly, the use of new energy storage components must be eliminated by creating accurate models for estimating energy output based on climate conditions [8]. Several ANNs are employed to estimate, evaluate, and moderate the negative effects of weather conditions on wind turbine generation power for the purpose of trying to estimate the maximum storage ability of the grid during the adoption rates of RESs, specifically wind turbine systems, and preserve the utility grid's performance indices within achievable limits, model meteorological factors for minimizing the generation negative aspects, and estimate the output power [9].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Sharkawy,

Ameen,

Mohamed

et al. 2024

Automation

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The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine’s output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days’ worth of data were utilized to train the three created NNs, and the last 12 days’ worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology.

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Section: Introduction 1backgroundmentioning

confidence: 99%