Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms

Lv, Jiaqing; Zheng, Xiaodong; Pawlak, Mirosław; Mo, Weike; Miśkowicz, Marek

doi:10.1016/j.renene.2021.05.123

Cited by 26 publications

(15 citation statements)

References 51 publications

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“…The forecasting horizon typically ranges from one hour to 48 h [68]. Reviewed works [24–25, 27–29, 31, 34, 36–38] and [40–65] investigate short‐term forecasting through different models and methodologies, using different input data and different error metrics.…”

Section: Modelsmentioning

confidence: 99%

“…Improving the accuracy of ultra‐short‐term forecasting models could play a decisive role in the operation and planning of power systems, since it could reduce the spinning reserve cost and further improve the structure of the power grid [67]. Reviewed works [25,28–29, 37, 44, 47, 54–55, 57, 60] and [63–65] investigate ultra‐short‐term forecasting through different models and methodologies.…”

Section: Modelsmentioning

confidence: 99%

“…Various wind power probabilistic forecasting models are proposed in the reviewed works [24–65]. All these works focus on short‐term wind power forecasting.…”

Section: Short‐term Wppf Complexity and Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Bazionis

Karafotis

Georgilakis

2021

IET Renewable Power Gen

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A review of state‐of‐the‐art short‐term wind power probabilistic forecasting models is the focus here. The improvement of the accuracy and efficiency of probabilistic forecasting models has been in the centre of attention of researchers in recent years, since the need to further comprehend and efficiently use the uncertainty of forecasts is increasing. Since the optimal operation and control of energy systems and electricity markets is one of the important aspects of performing wind power forecasts, this review focuses in short‐term probabilistic forecasting models, which could prove to be useful in the daily planning and operation of power systems. The short‐term concept of forecasts is analysed in detail, along with the case studies and examples proposed by the reviewed literature. The key advantages and disadvantages of the reviewed probabilistic forecasting methodologies are identified. Furthermore, different classifications of the reviewed works according to the data that are used to provide an accurate forecasting model are also provided. Future directions in the field of short‐term wind power probabilistic forecasting are also proposed.

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Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

See 1 more Smart Citation

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

Bazionis

Karafotis

Georgilakis

2021

IET Renewable Power Gen

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show abstract

“…Compared with the traditional methods, machine learning model does not need to build control equations to describe the atmospheric motion, which can greatly reduce the time required for prediction [17][18][19]. Machine learning model can analyze meteorological data to achieve the purpose of prediction and is more and more used in the of wind field prediction [20][21][22][23]. Jianzhou Wang et al [24] proposed and compared 8 methods of wind speed prediction based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Method for Wind Fields Forecasting Utilizing GNSS Radio Occultation Data

et al. 2022

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With the development of computer technology and expanding environmental issues, machine learning has received more and more attention in the field of weather forecasting. Global Navigation Satellite System-Radio Occultation(GNSS-RO) technology is a kind of remote sensing technology. This investigation proposes an alternative to numerical weather forecasting model. The new method is based on machine learning utilizing GNSS-RO data to forecast the wind field in the Beijing-Tianjin-Hebei region of China. The dataset including temperature, humidity， pressure， wind speed and direction was obtained by numerical calculation in terms of historical monitoring data in Beijing-Tianjin-Hebei region. Then the models of wind fields forecasting based on machine learning were established with different neural network including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Deep Neural Networks (DNN). The prediction performance of different models was analyzed. The results demonstrate that LSTM and CNN have better performance on predicting the wind field than Deep Neural Networks. The wind speed error is about 1.4m/s, and the wind direction error is about 30°. Moreover, the time required for neural network to predict a new sample is about 1 second, which is only 0.2% of the prediction time compared with numerical model. Finally, the machine learning model can be used to predict the wind field effectively, with GNSS-RO data as the input in application. This paper pro-vides a new method in sight to use machine learning to forecast the regional wind field utilizing GNSS-RO data. INDEX TERMSWind fields forecasting; machine learning; GNSS-RO; Long Short-Term Memory (LSTM); Convolutional Neural Networks (CNN)

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“…Since then, the Lasso has become a popular approach in modern-day high-dimensional statistics [4]. It has been applied in biometrics [22], power systems [20,23], energy [21], etc. So far as we know, it has not been applied in pollutant modeling areas yet.…”

Section: Introductionmentioning

confidence: 99%

Prediction of daily maximum ozone levels using Lasso sparse modeling method

Lv¹,

2020

Preprint

Self Cite

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This paper applies modern statistical methods in prediction of the next-day maximum ozone concentration, as well as the maximum 8-hour-mean ozone concentration of the next day. The model uses a large number of candidate features, including the present day's hourly concentration level of various pollutants, as well as the meteorological variables of the present day's observation and the future day's forecast values. In order to solve such an ultra-high dimensional problem, the least absolute shrinkage and selection operator (Lasso) was applied. The 1 nature of this methodology enables the automatic feature dimension reduction, and a resultant sparse model. The model trained by 3-years data demonstrates relatively good prediction accuracy, with RMSE= 5.63 ppb, MAE= 4.42 ppb for predicting the next-day's maximum 3 concentration, and RMSE= 5.68 ppb, MAE= 4.52 ppb for predicting the next-day's maximum 8-hour-mean 3 concentration. Our modeling approach is also compared with several other methods recently applied in the field, and demonstrates superiority in the prediction accuracy.

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Very short-term probabilistic wind power prediction using sparse machine learning and nonparametric density estimation algorithms

Cited by 26 publications

References 51 publications

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

A review of short‐term wind power probabilistic forecasting and a taxonomy focused on input data

A Machine Learning-Based Method for Wind Fields Forecasting Utilizing GNSS Radio Occultation Data

Prediction of daily maximum ozone levels using Lasso sparse modeling method

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