Forecasting Day-Ahead Solar Radiation Using Machine Learning Approach

Hassan, Md. Ziaul; Ali, Md. Erfan Khandakar; Ali, Ahmed; Kumar, Jashnil

doi:10.1109/apwconcse.2017.00050

Cited by 34 publications

(15 citation statements)

References 12 publications

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“…The best model recommended by this work had an R 2 of 0.89 and an RMSE of 0.729. In [7], a day-ahead solar forecasting was done using a support vector machine with different kernels, among which the radial basis function performed best with an RMSE of 0.580 and a mean absolute error (MAE) of 0.728. Neural networks were used by [8] to forecast solar irradiance, combined with a genetic algorithm to find the optimal array size and position of the solar monitoring station to get the most accurate forecast.…”

Section: Related Workmentioning

confidence: 99%

Regression and Generalized Additive Model to Enhance the Performance of Photovoltaic Power Ensemble Predictors

Sundararajan

Ollis

2021

IEEE Access

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Photovoltaic (PV) power prediction has a constantly evolving solutions landscape with a myriad of data-driven techniques. Each technique leverages a self-adaptive algorithm that must retrain in intervals, be it each day, week, or season, to avoid the model generalizing poorly because of overfitting, underfitting, or concept drift. This paper aims to improve the generalization capability of PV power predictors such as autoencoders used widely in the industry by introducing feature-enhanced ensemble learning (FEEL) after the feature selection step. This framework uses a combination of nonparametric regression and generalized additive models, and an ensemble of weak regularized multilayer perceptron models. Once trained, the framework can reliably generalize on test data across long time periods without any significant degradation in performance. The proposed framework was validated against the baseline autoencoder-based feature enhancement model on a real PV system from a smart neighborhood in Alabama for September 2019. The FEEL framework performed three times better than the baseline, but when applied to the baseline, its performance improved by two times on average. Furthermore, the framework generalized consistently better than five other feature enhancement strategies. Despite fluctuations in weather, the FEEL framework's R-square score had a range of 8.1%, whereas that of the baseline was 48.3%. The mutual information and Minkowski distance scores attempted to quantify concept and model drift, respectively. These scores show that the FEEL framework generalized the ensemble learning models at least two times better than the baseline across the different test days. These results form the first step toward decentralized intelligence for smart grid applications that could free up resources for other expensive analytics in the field. INDEX TERMSFeature enhancement, nonparametric regression, ensemble learning, generalized additive model, concept drift, photovoltaic power prediction. Nomenclature Unless otherwise specified, the following nomenclature is applied throughout this paper. Abbreviations AE Autoencoder, also denotes one of the feature enhancement strategies ARIMA Autoregressive integrated moving average ARIMAX ARIMA with extragenous inputs ERM Ensemble of regularized multilayer perceptrons FEEL Feature-enhanced ensemble learning GAM Generalized additive model

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Section: Related Workmentioning

confidence: 99%

Regression and Generalized Additive Model to Enhance the Performance of Photovoltaic Power Ensemble Predictors

Sundararajan

Ollis

2021

IEEE Access

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“…The kernel parameters must be ideal to solve the problem classification according to the data to become separable in the next space. The four principal basic kernel functions are linear, polynomial, radial basis function (RBF) and sigmoid [57,60].…”

Section: Kernel Function Provided For Svmmentioning

confidence: 99%

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

et al. 2021

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The use of intelligent algorithms for global solar prediction is an ideal tool for research focused on the use of solar energy. Forecasting solar radiation supports different applications focused on the generation and transport of energy in places where there are no meteorological stations. Different solar radiation prediction techniques have been applied in different time horizons, such as neural networks (ANN) or machine learning (ML), with the latter being the most important. The support vector machine (SVM) is a classification method of the ML that is used to predict solar radiation. To obtain a better accuracy of prediction data, search optimization algorithms (SOA) such as genetic algorithms (GA) and the particle swarm optimization algorithm (PSO) were used to optimize the prediction accuracy by searching the model parameters. This article presents a review of different hybrid SVM models with SOA applied to obtain the best parameters to reduce the prediction error of solar radiation using meteorological variables. Research articles from the last 5 years on solar radiation prediction using SVM models and hybrid SMV optimized models with SOA were studied. The results show that SVM with GA presents a better performance than the classical SVM models using the Radial basis kernel function for prediction parameters.

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“…For developing prediction models, a variety of regression algorithms are tested, including linear least squares and support vector machines with various kernel functions. We use day-ahead sun radiation data forecasts in these tests to show that a machine learning approach can correctly anticipate short-term solar power [2]. A hybrid or mixed forecasting method was developed by combining clustering, classification, and regression approaches to produce a forecasting model.…”

Section: Related Workmentioning

confidence: 99%

Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

Anuradha¹,

Erlapally²,

Karuna³

et al. 2021

E3S Web Conf.

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Solar power is generated using photovoltaic (PV) systems all over the world. Because the output power of PV systems is alternating and highly dependent on environmental circumstances, solar power sources are unpredictable in nature. Irradiance, humidity, PV surface temperature, and wind speed are only a few of these variables. Because of the unpredictability in photovoltaic generating, it’s crucial to plan ahead for solar power generation as in solar power forecasting is required for electric grid. Solar power generation is weather-dependent and unpredictable, this forecast is complex and difficult. The impacts of various environmental conditions on the output of a PV system are discussed. Machine Learning (ML) algorithms have shown great results in time series forecasting and so can be used to anticipate power with weather conditions as model inputs. The use of multiple machine learning, Deep learning and artificial neural network techniques to perform solar power forecasting. Here in this regression models from machine learning techniques like support vector machine regressor, random forest regressor and linear regression model from which random forest regressor beaten the other two regression models with vast accuracy.

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Forecasting Day-Ahead Solar Radiation Using Machine Learning Approach

Cited by 34 publications

References 12 publications

Regression and Generalized Additive Model to Enhance the Performance of Photovoltaic Power Ensemble Predictors

Regression and Generalized Additive Model to Enhance the Performance of Photovoltaic Power Ensemble Predictors

Hybrid Techniques to Predict Solar Radiation Using Support Vector Machine and Search Optimization Algorithms: A Review

Analysis Of Solar Power Generation Forecasting Using Machine Learning Techniques

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