Random forest ensemble of support vector regression models for solar power forecasting

Abuella, Mohamed; Chowdhury, Badrul

doi:10.1109/isgt.2017.8086027

Cited by 39 publications

(19 citation statements)

References 16 publications

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“…A way to reduce the covariance between each tree is needed, which is accomplished by using random forests. The studies of using PV forecasting with a random forest can be found in references [31][32][33][34].…”

Section: Random Forestmentioning

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: Random Forestmentioning

confidence: 99%

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

Choi¹,

Hur

2020

Energies

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“…Article [72,73] applied the technique to model short-term electrical load forecasting. In [74], random forest was used for solar power forecasting. Furthermore, adding to their benefit, random forests produce variable importance measures for each predictor variable [75].…”

Section: Random Forestmentioning

confidence: 99%

Potential of aggregated escalator loads in demand response

Uimonen

Tukia

Siikonen³

et al. 2019

Electric Power Systems Research

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“…(2)) makes the time series stationary hence it can be introduced in a machine learning tool such as regression tree forecasting. A lot of methods of prediction based on machine learning are available, interested readers can refer to [34] concerning on random forest ensemble of support vector regression models, to [35] about Kalman filter and regressor, to [36] for works related to the Kriging, NWP and gradient boosted regression tree and to [37] for a very interesting evaluation of statistical learning configurations.…”

Section: The Prediction Modelsmentioning

confidence: 99%

Prediction intervals for global solar irradiation forecasting using regression trees methods

et al. 2018

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A global horizontal irradiation prediction (from 1 hour to 6 hours) is performed using 2 persistence models (simple and "smart" ones) and 4 machine learning tools belonging to the regression trees methods family (normal, pruned, boosted and bagged). A prediction band is associated to each forecast using methodologies based on: bootstrap sampling and k-fold approach, mutual information, stationary time series process with clear sky model, quantiles estimation and cumulative distribution function. New reliability indexes (gamma index and gamma test) are built from the mean interval length (MIL) and prediction interval coverage probability (PCIP). With such methods and error metrics, good prediction bands are estimated for Ajaccio (France) with a MIL close to 113 Wh/m², a PCIP reaching 70% and a gamma index lower than 0.9.

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Random forest ensemble of support vector regression models for solar power forecasting

Cited by 39 publications

References 16 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

Potential of aggregated escalator loads in demand response

Prediction intervals for global solar irradiation forecasting using regression trees methods

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