Hybrid machine learning forecasting of solar radiation values

Gala, Yvonne; Fernández, Ángela; Díaz, Julia; Dorronsoro, José R.

doi:10.1016/j.neucom.2015.02.078

Cited by 92 publications

(32 citation statements)

References 18 publications

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“…In contrary with supervised learning, an unsupervised learning model does not need outputs. It is able to find hidden structure in its inputs [20]. The basic concept of ensemble learning is to train multiple base learners as ensemble members, and to combine their predictions into a single output.…”

Section: Machine Learningmentioning

confidence: 99%

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Choi¹,

Hur

2020

Energies

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As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.

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Section: Machine Learningmentioning

confidence: 99%

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Choi¹,

Hur

2020

Energies

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“…Additionally, all the outputs of these forecasting sub-models are taken into consideration to determine the best output of the ensemble model. This method can well leverage the advantages of different forecasting sub-models to achieve the performance optimization of the ensemble model to provide better forecasting results for application [32,33].…”

Section: Literature Reviewmentioning

confidence: 99%

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

et al. 2018

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Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models.

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“…Similarly, Deo et al [201] found that coupling of SVM with WT model had improved its short-term solar irradiance forecasting performance by 10.68%. Another hybrid model comprising of SVR, RF and Gradient Boosted Regression (GBR) was developed for 3-h aggregate and daily solar irradiance forecasting with 1% improvement compared to these individual models [202]. Apart from that, Aguiar et al [203] improved the ANN solar forecasting method using satellite data, whereby 4-5% average improvements were observed for case studies involving Pozo Izquierdo and Las Palmas stations.…”

Section: Referencesmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

243

105

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Hybrid machine learning forecasting of solar radiation values

Cited by 92 publications

References 18 publications

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

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