Random Forests and Gradient Boosting for Wind Energy Prediction

Alonso, A.; Dorronsoro, José R.

doi:10.1007/978-3-319-19644-2_3

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…RF is derived from an ensemble of decision trees. The decision tree (DT), also known as a classification and regression tree (CART), is comprised of a collection of nodes that organize information extracted from a training dataset into a hierarchical structure (Alonso et al, 2015). The structure of a DT starts with a node called the root tree, which encompasses the entire learning sample.…”

Section: Random Forest Model For Medium-term Forecastingmentioning

confidence: 99%

Medium-term wind power forecasting using reduced principal component analysis based random forest model

Jamii,

Trabelsi,

Mansouri

et al. 2024

Wind Engineering

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Due to its dependence on weather conditions, wind power (WP) forecasting has become a challenge for grid operators. Indeed, the dispatcher needs to predict the WP generation to apply the appropriate energy management strategies. To achieve an accurate WP forecasting, it is important to choose the appropriate input data (weather data). To this end, a medium-term wind power forecasting using reduced principal component analysis (RKPCA) based Random Forest Model is proposed in this paper. Two-stage WP forecasting model is developed. In the first stage, a Kernel Principal Component Analysis (KPCA) and reduced KPCA (RKPCA)-based data pre-processing techniques are applied to select and extract the important input data features (wind speed, wind direction, temperature, pressure, and relative humidity). The main idea behind the RKPCA technique is to use Euclidean distance for reducing the number of observations in the training data set to overcome the problem of computation time and storage costs of the conventional KPCA in the feature extraction phase. In the second stage, a Random Forest (RF) algorithm is proposed to predict the WP for medium-term. To evaluate the performance of the proposed RKPCA-RF technique it has been applied to data extracted from NOAA’S Surface Radiation (SURFRAD) network at Bondville station, located in USA. The presented results show that the proposed RKPCA-RF technique achieved more accurate results than the state-of-the-art methodologies in terms of RMSE (0.09), MAE (0.23), and R2 (0.85). In addition, the proposed technique achieved the lowest overall computation time (CPU).

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Section: Random Forest Model For Medium-term Forecastingmentioning

confidence: 99%

Medium-term wind power forecasting using reduced principal component analysis based random forest model

Jamii,

Trabelsi,

Mansouri

et al. 2024

Wind Engineering

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“…Andrade and Bessa (2017) applied a gradient boosting tree to wind and solar forecasting and obtained a significant improvement over prior models. Alonso et al (2015) attempted to solve the issues of wind energy prediction with random forest and GB algorithms. They revealed that both algorithms can improve the accuracy of the prediction but GB can handle significantly higher data volumes.…”

Section: Research On Gradient Boosting Machinementioning

confidence: 99%

A gradient boosting machine-based framework for electricity energy knowledge discovery

Xie

Zhu²,

Zhao³

et al. 2022

Front. Environ. Sci.

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Knowledge discovery in databases (KDD) has an important effect on various fields with the development of information science. Electricity energy forecasting (EEF), a primary application of KDD, aims to explore the inner potential rule of electrical data for the purpose to serve electricity-related organizations or groups. Meanwhile, the advent of the information society attracts more and more scholars to pay attention to EEF. The existing methods for EEF focus on using high-techs to improve the experimental results but fail to construct an applicable electricity energy KDD framework. To complement the research gap, our study aims to propose a gradient boosting machine-based KDD framework for electricity energy prediction and enrich knowledge discovery applications. To be specific, we draw on the traditional knowledge discovery process and techniques to make the framework reliable and extensible. Additionally, we leverage Gradient Boosting Machine (GBM) to improve the efficiency and accuracy of our approach. We also devise three metrics for the evaluation of the proposed framework including R-square (R2), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Besides, we collect the electricity energy consumption (EEC) as well as meteorological data from 2013 to 2016 in New York state and take the EEC prediction of New York State as an example. Finally, we conduct extensive experiments to verify the superior performance of our framework and the results show that our model achieves outstanding results for the three metrics (around 0.87 for R2, 60.15 for MAE, and 4.79 for MAPE). Compared with real value and the official prediction model, our approach also has a remarkable prediction ability. Therefore, we find that the proposed framework is feasible and reliable for EEF and could provide practical references for other types of energy KDD.

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“…In [134], both RF and Gradient Boosting are proposed for wind energy prediction. This work experimentally show that both ensemble methods can improve the performance of SVR for individual wind farm energy prediction.…”

Section: Rf For Wind Speed Prediction Problemsmentioning

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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Random Forests and Gradient Boosting for Wind Energy Prediction

Cited by 13 publications

References 6 publications

Medium-term wind power forecasting using reduced principal component analysis based random forest model

Medium-term wind power forecasting using reduced principal component analysis based random forest model

A gradient boosting machine-based framework for electricity energy knowledge discovery

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

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