Comparative performance of AI methods for wind power forecast in Portugal

Godinho, Miguel; Castro, Rui

doi:10.1002/we.2556

Cited by 21 publications

(16 citation statements)

References 29 publications

(46 reference statements)

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“…Artificial intelligence models are commonly used in short-term wind power forecasting because of the benefits in dependency analysis and pattern detection [25][26][27][56][57][58][59][60]. Ozkan et al [61] suggested a multi-feature convolutional neural network-based wind power forecasting approach.…”

Section: Hybrid Forecasting Modeling Based On Neural Network-related Approachesmentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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As wind and photovoltaic energy become more prevalent, the optimization of power systems is becoming increasingly crucial. The current state of research in renewable generation and power forecasting technology, such as wind and photovoltaic power (PV), is described in this paper, with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting. The methods for forecasting wind power and PV production. The physical model, statistical learning method, and machine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production. Moreover, the experiments demonstrated that cloud map identification has a significant impact on PV generation. With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes, this paper summarizes the classification of wind power and PV generation systems, as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

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Section: Hybrid Forecasting Modeling Based On Neural Network-related Approachesmentioning

confidence: 99%

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

Xu¹,

Shao²,

Deng³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…Radial basis function network with orthogonal least squares also perform well compared to other learning algorithms. 23…”

Section: Renewable Energy Forecastingmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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“…One is adopting the filter algorithm such as Kalman filter, Volterra adaptive filter, Bilateral filter, etc to the regression model which has been widely applied in the prediction on periodic data, stationary data and other data with clear expected distribution. It is worth noting that machine learning integrated with filter algorithm has been a powerful tool for prediction and optimal problems in high precision and efficiency [37][38][39]. The other is to regress the random distribution of data set after separating the expected distribution, which is applied by Dual-LSTM model in this research.…”

Section: High Noise Time Seriesmentioning

confidence: 99%

“…From the perspective of emerging energy supply which features expected distribution uncertain and high noise, deep learning model is desirable for whose regression analyse [33][34]. Nevertheless, the application of deep learning algorithms on high noise or linear time series prediction were rarely studied due to that the deep learning algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods [35][36][37]. Moreover, overfitting frequently occurs while specifically predicting emerging energy supply [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Reliable Prediction on Emerging Energy Supply for National Sustainability and Stability: A Case Study on Coal Bed Gas Supply in China Based on the Dual-LSTM Model

Kang

Lao

et al. 2021

IEEE Access

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Aiming to prevent from the imbalance between supply and demand of energy in which the share of emerging type is rapidly increasing, to predict the supply of emerging energy reliably is significant. However, the expected distribution uncertain and high-noise characteristics of emerging energy supply impede the reliable prediction. The Dual-LSTM (Long Short-Term Memory) model was constructed for the characteristic extracting and effective prediction of the expected distribution uncertain high-noise emerging energy supply time series. A case study on coal bed gas supply in China was conducted. Results showed that the Dual-LSTM model effectively solved the the problem of superfluous and non-quantifiable variables in the prediction of coal bed gas supply and extracted the statistical characteristics of expected distribution uncertain and high-noise data samples effectively with a relative error major less than 5% in short-term. Besides, the Dual-LSTM model has a significantly higher prediction accuracy while comparing with ARIMA model and original LSTM model. Ultimately, it is predicted that the year-on-year growth rates of coal bed gas supply of China from January to September, 2021, approximately maintains 75% in average based on the Dual-LSTM model. The Dual-LSTM model provides a reliable statistical model for policy decision to maintain national sustainability and stability. INDEX TERMSImproved deep learning model; Uncertain expected distribution high-noise sample; Emerging energy; Supply-demand stability; Reliable prediction; Time series analysis

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Comparative performance of AI methods for wind power forecast in Portugal

Cited by 21 publications

References 29 publications

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of theWind Power and Photovoltaic Energy

A holistic review on energy forecasting using big data and deep learning models

Reliable Prediction on Emerging Energy Supply for National Sustainability and Stability: A Case Study on Coal Bed Gas Supply in China Based on the Dual-LSTM Model

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