Solar irradiance forecasting model based on extreme learning machine

Burianek, Tomas; Mišák, Stanislav

doi:10.1109/eeeic.2016.7555445

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…Specifically, 1-day-ahead hourly forecasting of PV power output in Shanghai, China, considering different models depending on the weather conditions (sunny days, cloudy days, and rainy days). Another work dealing with PV systems production prediction with ELMs is [56], in this case meteorological input variables, mainly cloudiness, are considered. In [57] a hybrid approach involving entropy method and ELMs were applied to a prediction problem of short-term PV power generation.…”

Section: Elms In Pv Power Systems Production Predictionmentioning

confidence: 99%

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Ser¹,

Casillas-Pérez²,

Cornejo-Bueno³

et al. 2021

Preprint

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Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining predic-

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Section: Elms In Pv Power Systems Production Predictionmentioning

confidence: 99%

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Ser¹,

Casillas-Pérez²,

Cornejo-Bueno³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…• As more research is done, artificial intelligence (AI) and machine learning have gained much interest regarding solar irradiation forecast models. Artificial neural network (ANN) (Premalatha and Valan Arasu 2016;Benali et al 2019) , support vector machines (SVM) (Wang et al 2019) , extreme learning machine (ELM) (Burianek and Misak 2016), wavelet transform (Yadav and Behera 2014), deep learning (Lai et al 2021), and ensemble learning are a few examples of typical AI techniques (Voyant et al 2017;Alkhayat and Mehmood 2021). These methods can handle the non-linearity presented in the solar irradiation time series, and usually have high accuracy compared to statistical models.…”

Section: Introductionmentioning

confidence: 99%

Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

Benmouiza

2022

Environ Sci Pollut Res

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The foundation for many solar energies uses as well as economic and environmental concerns is global solar irradiation information. However, due to solar irradiation and measurements variations, reliable worldwide statistics on solar irradiation are frequently impossible or difficult to acquire. In addition, more precise forecast of solar irradiation plays an increasingly important role in electric energy planning and management due to integrating photovoltaic solar systems into power networks. Hence, this paper proposes a new hybrid model for 1-hour ahead solar irradiation forecasting called LGC-GMDH (local gravitational clustering-Group method of data handling). The novel LGC-GMDH model is based on the local clustering that adequately captures the underlying features of the solar irradiation time series. Each cluster is then forecasted using the GMDH method, which is a self-organized system that is capable of handling very complicated nonlinear problems. Finally, these local forecasts are reconstructed in order to obtain the global forecast. Comparative study between the proposed model and the traditional individual models such as; backpropagation neural network (BP), supporting vector machines (SVM), long short-term memory (LTSM), hybrid models such; BP-MLP, RNN-MLP, LSTM-MLP hybrid wavelet packet decomposition (WPD), convolutional neural network (CNN) with LSTM-MLP, and ANFIS clustering shows that the proposed model overcomes conventional model deficiencies and achieves more precise predicting outcome.

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“…Increasing the predictability of electricity generation is an interesting alternative, as it brings more information to energy planning and reduces the total generation costs to meet the demand. ML algorithms have helped to make more accurate and earlier predictions in planning [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation

Viscondi

Alves-Souza

2021

Energies

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Forecasting photovoltaic electricity generation is one of the key components to reducing the impacts of solar power natural variability, nurturing the penetration of renewable energy sources. Machine learning is a well-known method that relies on the principle that systems can learn from previously measured data, detecting patterns which are then used to predict future values of a target variable. These algorithms have been used successfully to predict incident solar irradiation, but the results depend on the specificities of the studied location due to the natural variability of the meteorological parameters. This paper presents an extensive comparison of the three ML algorithms most used worldwide for forecasting solar radiation, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), aiming at the best prediction of daily solar irradiance in a São Paulo context. The largest dataset in Brazil for meteorological parameters, containing measurements from 1933 to 2014, was used to train and compare the results of the algorithms. The results showed good approximation between measured and predicted global solar radiation for the three algorithms; however, for São Paulo, the SVM produced a lower Root-Mean-Square Error (RMSE), and ELM, a faster training rate. Using all 10 meteorological parameters available for the site was the best approach for the three algorithms at this location.

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Solar irradiance forecasting model based on extreme learning machine

Cited by 10 publications

References 7 publications

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation

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