Prediction of Solar Energy Potential with Artificial Neural Networks

Göksu, Burak; Bayraktar, Murat; Pamık, Murat

doi:10.1007/978-3-030-20637-6_13

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The LM is an iterative algorithm that determines the minimum of a multivariable function expressed as the sum of squares of nonlinear real-valued functions [34]. The LM is characterized by its quick learning feature [33].…”

Section: Levenberg-marquart Algorithmmentioning

confidence: 99%

Effect of Environmental Conditions and Training Algorithms on the Efficiency of a NARX Based Approach to Predict PV Panel Power Output

2020

IJEM

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Photovoltaic energy is volatile in nature since it depends on weather conditions. It is important to have an idea about the reliability and the economic feasibility of any new project to decide whether it is right to proceed with the installation of such a project. Hence, it is becoming fundamental to know renewable energy state and production that can be combined with other less variable and more predictable sources to justify the choice of regions for the new photovoltaic projects installation. The current research investigates the forecasting abilities of a NARX based approach. The influence of the meteorological data, such as irradiance, ambient temperature, and wind speed, and the impact of training algorithms on the performance of the NARX-based forecaster is studied. For this purpose, four models are discussed, each model is trained based on three training algorithms. The NARX model using a Bayesian Regularization algorithm, trained by the three meteorological data as inputs and the converted power output as output, outperforms the other models. It consists of a simple architecture with one input layer, a hidden layer containing 1O neurons, and an output layer, with a mean square error of 0.0085 W2 for the training phase and 0.0043 W2 testing phase, and the overall regression of 95.48%. This simplified architecture and low values of the mean square error and the regression coefficient suggest that they are promising photovoltaic output prediction tools, particularly in locations where few meteorological parameters are monitored.

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Section: Levenberg-marquart Algorithmmentioning

confidence: 99%