A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models

Amoura, Yahia; Pereira, Ana I.; Lima, José

doi:10.1007/978-3-030-97027-7_12

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…However to predict the solar irradiation the predictors used are: time sampling, relative humidity, temperature and wind speed. To forecast the future value of wind speed and as per of research experience made in [7] and actual data correlation, the used predictors were: time, solar irradiation, temperature and previous recorded wind speed.…”

Section: Data Correlationmentioning

confidence: 99%

“…The inquiry of this resources, especially, wind speed and solar irradiation depends on large sizes of data and parameters availability [6]. Nowadays, Supervised Machine Learning (ML) methods are widely provided to achieve an accurate forecast model for wind speed and solar irradiation [7]. a model with inputs and their desired outputs is given to learn a general rule that maps inputs to outputs.…”

Section: Introductionmentioning

confidence: 99%

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Combined Optimization and Regression Machine Learning for Solar Irradiation and Wind Speed Forecasting

Amoura

Torres

Lima

et al. 2022

Communications in Computer and Information Science

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Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.

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Section: Data Correlationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%