Modeling a robust wind-speed forecasting to apply to wind-energy production

Hernández-Travieso, José Gustavo; González, Carmelo Javier León; Alonso-Hernández, Jesús B.; Canino-Rodríguez, José M.; Ravelo-García, Antonio G.

doi:10.1007/s00521-018-3619-6

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In our study, we focus on a short-term wind speed forecasting project in China, which is a type of energy forecasting field. A highly accurate wind speeding forecasting can effectively improve the maintenance of smart grid and protect the turbine in wind farms [25,27]. However, wind forecasting has always been a difficult task because of the outliers in the wind series provided, which are generated by the characteristics of intermittent and random of wind speed.…”

Section: Introductionmentioning

confidence: 99%

Robust penalized extreme learning machine regression with applications in wind speed forecasting

Yang

Zhou

Gao

et al. 2021

Neural Comput & Applic

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In extreme learning machine (ELM) framework, the hidden layer setting determines its generalization ability; and in presence of outliers in the training set, weights between hidden layer and output layer based on the least squares would be overly estimated. To address these two problems in ELM implementation, we extend robust penalized statistical framework in ELM and propose a general robust penalized ELM, which consists of two components (robust loss function and regularization item), for regression to improve the efficiency of ELM training with more elegant neural network structure resulting in more accurate predictions. We investigate six different loss functions (l 1 -norm loss, l 2 -norm loss, Huber loss, Bisquare loss, exponential squared loss and Lncosh loss) and two regularization strategies (lasso penalty and ridge penalty). Furthermore, we present two training procedures for our robust penalized ELM via iterative reweighted least squares method with hyper-parameter setting by cross-validation with lasso penalty and ridge penalty, respectively. Finally, the proposed robust penalized ELM is employed in an ultra-short-term wind speed forecasting study, and our framework is confirmed in this specific application producing more effective predictions according to the multi-step forecasting performance.

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Section: Introductionmentioning

confidence: 99%

Robust penalized extreme learning machine regression with applications in wind speed forecasting

Yang

Zhou

Gao

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…The best results are presented by the adaptive neuro-fuzzy inference system. Another work using the NN multilayer perceptron feed-forward is presented in [24]. The training method implemented is the backpropagation algorithm, which uses various past observations of different phenomena, such as temperature, precipitation, and time velocity, to carry out the forecast for wind speed.…”

Section: Introductionmentioning

confidence: 99%

Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

2021

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Wind energy is one of the most promising alternatives as energy sources; however, to obtain the best results, producers need to forecast the wind speed, generated power and energy price in order to provide the appropriate tools for optimal operation, planning, control and marketing both for isolated wind systems and for those that are interconnected to a main distribution network. For the present work, a novel methodology is proposed for the forecasting of time series in wind energy systems; it consists of a high-order neural network that is trained on-line by the extended Kalman filter algorithm. Unlike most modern artificial intelligence methods of forecasting, which are based on hybridizations, data pre-filtering or deep learning methods, the proposed method is based on the simplicity of implementation, low computational complexity and real-time operation to produce 15-step-ahead forecasting in a time series of wind speed, generated power and energy price. The proposed scheme has been evaluated using real data from open access repositories of wind farms. The results show that an on-line training of the neural network produces high precision, without the need for any other information beyond a few past observations.

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A Review on Machine Learning Techniques in IoT-Based Smart Grid Applications

Goyal,

Ahmed

2024

Communications in Computer and Information Science

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Modeling a robust wind-speed forecasting to apply to wind-energy production

Cited by 5 publications

References 16 publications

Robust penalized extreme learning machine regression with applications in wind speed forecasting

Robust penalized extreme learning machine regression with applications in wind speed forecasting

Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

A Review on Machine Learning Techniques in IoT-Based Smart Grid Applications

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