Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

Dong, Wei; Yang, Qiang; Fang, Xinli

doi:10.3390/en11081975

Cited by 27 publications

(11 citation statements)

References 44 publications

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“…Local linear embedding (LLE) is a non-linear dimensionality reduction algorithm based on stream shape. It is able to achieve the dimensionality reduction and visualization of high-dimensional data without destroying the structure of the data stream shape [19,20]. The local information of the original data is preserved while removing the redundant information, which is almost unaffected by the noise value [21].…”

Section: Matrix Dimensionality Reduction Based On Local Linear Embedd...mentioning

confidence: 99%

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

Wang,

Zhong,

et al. 2023

Energies

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Regarding the generation and integration of typical scenes of PV and loads in urban photovoltaic distribution networks, as well as the insufficient consideration of the spatiotemporal correlation between PV and loads, this paper proposes a typical scene extraction method based on local linear embedding, kernel density estimation, and a joint PV–load typical scene extraction method based on the FP-growth algorithm. Firstly, the daily operation matrices of PV and load are constructed by using the historical operation data of PV and load. Then, the typical scenes are extracted by the dimensionality reduction of local linear embedding and the kernel density estimation method. Finally, the strong association rules of PV–meteorological conditions and load–meteorological conditions are mined based on the FP-growth algorithm, respectively. The association of PV–load typical daily operation scenarios is completed using meteorological conditions as a link. This experiment involved one year of operation data of a distribution network containing PV in Qingyuan, Guangdong Province. The typical scene extraction joint method, Latin hypercube sampling method, and k-means clustering-based scene generation method proposed in this paper are used for comparison, respectively. The results show that compared to the other two scenario generation methods, the error between the typical scenario obtained by this method and the actual operating scenario of the distribution network is smaller. The extracted typical PV and load scenarios can better fit the actual PV and load operation scenarios, which have more reference value for the operation planning of actual distribution networks containing PV.

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Section: Matrix Dimensionality Reduction Based On Local Linear Embedd...mentioning

confidence: 99%

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

Wang,

Zhong,

et al. 2023

Energies

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Section: Photovoltaic and Wind Energymentioning

confidence: 99%

“…The standard procedure for predicting energy production from wind farms is to use NWP models and power curves of installed wind turbines, although many applications also use machine learning techniques. The research in this field has, in recent years, focused on using new machine learning methods [14][15][16][17], different NWP models and configurations, such as ensemble forecasting [18,19], or different approaches, from forecasting wind power for every wind turbine with high-resolution NWP models [20] to wind power production over whole countries [21]. With the obvious limitations of accuracy of NWP models, authors are trying to build models using methods such as RF, XGB, ANN, and DL to increase the accuracy of very short-range forecasts up to few hours, most commonly examined dayahead forecasts, and predictions up to several days in advance.…”

Section: Photovoltaic and Wind Energymentioning

confidence: 99%

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

2022

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In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.

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“…These delays were selected using the wrapper method [66,67]. The predictions were performed for 1 (next month), 3 (next season), 6 (next semester), and 12 (next year) steps ahead, using the recursive prediction technique [68,69].…”

Section: Case Studymentioning

confidence: 99%

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

et al. 2020

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Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances.

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Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

Cited by 27 publications

References 44 publications

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

Extraction and Joint Method of PV–Load Typical Scenes Considering Temporal and Spatial Distribution Characteristics

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

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