Prediction of Solar Power Using Linear Regression

Thombare, Shubham; Pande, V. N.; Kulkarni, Ravi S.; Kakade, S. M.

doi:10.1007/978-981-16-9033-4_53

Cited by 5 publications

(2 citation statements)

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“…In [9], Ağbulut et al implemented and carried out the comparative study of different machine learning algorithms to predict global solar radiation on a daily basis. Thombare et al [10] used a linear regression model to predict the solar power output.…”

Section: Literature Surveymentioning

confidence: 99%

Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

Kulkarni,

Kakade,

Mawande

et al. 2023

J. Phys.: Conf. Ser.

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The paper proposes the use of Artificial Neural Networks (ANN) and Polynomial Regression (PR) algorithms for estimating the output of a Solar Photovoltaic (PV) Plant. The study compares the performance of these two methods, using a dataset of weather parameters and PV Plant output. The results show that both the methods can effectively estimate the PV Plant output, with the ANN model having a slightly higher accuracy than the polynomial regression model as ANNs can learn the nonlinear relationship between input stochastic parameters and output in a better manner. The study highlights the potential of machine learning techniques for optimizing the performance of solar PV plants and improving their efficiency.

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Section: Literature Surveymentioning

confidence: 99%

Estimation of Solar PV Plant output using Polynomial Regression and ANN Algorithm

Kulkarni,

Kakade,

Mawande

et al. 2023

J. Phys.: Conf. Ser.

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“…However, ANN is usually considered the "black box" and it is difficult to comprehend their results (Koeppe et al, 2021). Another popular statistical approach in modeling PV power generation is the multiple linear regression (MLR) model (EL-AAL et al, 2023;Yadav et al, 2022;Thombare et al, 2022;Limouni et al 2022). In comparison with ANN, the MLR model is relatively simple and easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

Alma'asfa,

Fraige,

Abdul Aziz

et al. 2024

Int. J. Renew. Energy Dev.

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The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.

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