Hour-ahead wind power forecast based on random forests

Lahouar, Ali; Slama, Jaleleddine Ben Hadj

doi:10.1016/j.renene.2017.03.064

Cited by 300 publications

(114 citation statements)

References 53 publications

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“…In parallel ensemble method with Bagging as a representative, the weak learners are generated in parallel, while in sequential ensemble method with Boosting as a representative, the weak learners are generated sequentially. It has been proven that an ensemble is usually much more accurate and reliable than a single learner (Lahouar and Ben Hadj Slama, 2017;Ma and Cheng, 2016;Natekin and Knoll, 2013;Wang et al, 2018;Zhou, 2012).…”

Section: Ensemble Methodsmentioning

confidence: 99%

Predicting wind pressures around circular cylinders using machine learning techniques

Kwok

2020

Journal of Wind Engineering and Industrial Aerodynamics

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Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20 th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to train models to predict wind pressures around circular cylinders. Wind pressures around smooth circular cylinders are a function of mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident wind, and circumferential angle of the cylinder. Considering these three parameters as the inputs, this study trained two ML models to predict mean and fluctuating pressures respectively. Three machine learning algorithms including decision tree regressor, random forest, and gradient boosting regression trees (GBRT) were tested. The GBRT models exhibited the best performance for predicting both mean and fluctuating pressures, and they are capable of making accurate predictions for Re ranging from 10 4 to 10 6 and Ti ranging from 0% to 15%. It is believed that the GBRT models provide very efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamic simulations for determining wind pressures around smooth circular cylinders within the studied Re and Ti range.

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Section: Ensemble Methodsmentioning

confidence: 99%

Predicting wind pressures around circular cylinders using machine learning techniques

Kwok

2020

Journal of Wind Engineering and Industrial Aerodynamics

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“…Several studies have stared at the forecast of wind speed for usage in the determination of accessible wind power. These studies were focused on principles such as fuzzy logic [7]- [10], neural networks [11], [12] and time series. the economic dispatch model to include wind-powered generators is created, using ideas based on fuzzy set theory.…”

Section: Related Workmentioning

confidence: 99%

Integration of Economic Load Dispatch with Wind Penetration

2019

IJITEE

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Wind energy is a form of inexhaustible energy that can be utilized deprived the use of fuel to produce energy and can save the fuel expenses of the electricity system. With rising oil rates and environmental contamination, it has now become clear that wind energy should be deemed a useful solution to the production of thermal energy. In order to continue with the quest for solutions to conventional energy technologies, it is essential to include wind penetration in the Economic load dispatch issue. This paper discloses a model to involve wind penetration in the Economic load dispatch issue and, in relation to the standard economic dispatch variables, includes the convergence to reduce the number of iterations.

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“…Random Forest (RF) is a novel machine learning algorithm developed in recent years [35][36][37][38]. RF is robust to the input and number of samples.…”

Section: Introductionmentioning

confidence: 99%

Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

Sun

Huang

et al. 2018

Sustainability

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Accurate load forecasting can help alleviate the impact of renewable-energy access to the network, facilitate the power plants to arrange unit maintenance and encourage the power broker companies to develop a reasonable quotation plan. However, the traditional prediction methods are insufficient for the analysis of load sequence fluctuations. The economic variables are not introduced into the input variable selection and the redundant information interferes with the final prediction results. In this paper, a set of the ensemble empirical mode is used to decompose the electricity consumption sequence. Appropriate economic variables are as selected as model input for each decomposition sequence to model separately according to its characteristics. Then the models are constructed by selecting the optimal parameters in the random forest. Finally, the result of the component prediction is reconstituted. Compared with random forest, support vector machine and seasonal naïve method, the example results show that the prediction accuracy of the model is better than that of the contrast models. The validity and feasibility of the method in the monthly load forecasting is verified.

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Hour-ahead wind power forecast based on random forests

Cited by 300 publications

References 53 publications

Predicting wind pressures around circular cylinders using machine learning techniques

Predicting wind pressures around circular cylinders using machine learning techniques

Integration of Economic Load Dispatch with Wind Penetration

Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

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