Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps

Qiao, Baihao; Liu, Jing; Wu, Peng; Teng, Yingzhi

doi:10.1016/j.asoc.2022.109586

Cited by 30 publications

(7 citation statements)

References 39 publications

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“…(4) The point prediction results for various levels of τ are utilized as inputs for kernel density estimation, the optimal bandwidth (h) is then determined through LOOCV, and finally, probability density predictions are achieved accordingly. (5) The results of probability density estimation are appropriately exploited to construct point and interval predictions, and the evaluation metrics of point and interval estimation of various models are separately obtained for comparison.…”

Section: Methodology Frameworkmentioning

confidence: 99%

“…With the development of wind power generation in recent years, significant research and progress have been made in the field of wind power forecasting (WPF). According to various modeling schemes, WPF can be essentially classified into physical models, statistical models, and artificial intelligence models with machine learning [3][4][5][6]. In more detail, physical methods commonly exploit long-term forecasts based on numerical weather predictions (NWPs).…”

Section: Introductionmentioning

confidence: 99%

“…In more detail, physical methods commonly exploit long-term forecasts based on numerical weather predictions (NWPs). Hence, many physical factors are required to achieve the best forecast accuracy [5], and physical models usually exhibit advantages in long-term forecasting [6]. Statistical methods for time-series forecasting include methods such as the Kalman filter (KF), autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH), and its variations [5].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, many physical factors are required to achieve the best forecast accuracy [5], and physical models usually exhibit advantages in long-term forecasting [6]. Statistical methods for time-series forecasting include methods such as the Kalman filter (KF), autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH), and its variations [5]. These methodologies are utilized for predicting the future production of wind power based on a large amount of historical data and are more effective than physical methods for short-term wind power forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Probability Density Forecasting of Wind Power Based on Transformer Network with Expectile Regression and Kernel Density Estimation

Xiao

2023

Electronics

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A comprehensive and accurate wind power forecast assists in reducing the operational risk of wind power generation, improves the safety and stability of the power system, and maintains the balance of wind power generation. Herein, a hybrid wind power probabilistic density forecasting approach based on a transformer network combined with expectile regression and kernel density estimation (Transformer-ER-KDE) is methodically established. The wind power prediction results of various levels are exploited as the input of kernel density estimation, and the optimal bandwidth is achieved by employing leave-one-out cross-validation to arrive at the complete probability density prediction curve. In order to more methodically assess the predicted wind power results, two sets of evaluation criteria are constructed, including evaluation metrics for point estimation and interval prediction. The wind power generation dataset from the official website of the Belgian grid company Elia is employed to validate the proposed approach. The experimental results reveal that the proposed Transformer-ER-KDE method outperforms mainstream recurrent neural network models in terms of point estimation error. Further, the suggested approach is capable of more accurately capturing the uncertainty in the forecasting of wind power through the construction of accurate prediction intervals and probability density curves.

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Section: Methodology Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probability Density Forecasting of Wind Power Based on Transformer Network with Expectile Regression and Kernel Density Estimation

Xiao

2023

Electronics

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show abstract

“…where Cj is the value of feature j, wj is its corresponding weight and wbias,i is its bias weight. As for the activation function of the graph nodes, the sigmoid function is adopted (14) since it is commonly used in other FCMs [38], [39].…”

Section: H Res Sizing Agentsmentioning

confidence: 99%

An Optimal Distributed RES Sizing Strategy in Hybrid Low Voltage Networks Focused on EVs’ Integration

et al. 2023

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The new era of the energy sector has already begun, and therefore, new challenges need to be tackled. A major challenge that residential distribution grids are going to encounter with the integration of photovoltaic (PV) panels and electric vehicles (EVs) is the unsynchronized new demand and the time-limited production of distributed generation, in combination with space limitations. Therefore, the necessity of energy storage systems (ESSs) is more than evident. ESSs have excessive manufacturing costs, implying that the purchase cost for residential users can be prohibitive. In the present work, a distributed optimal small-scale PV energy system sizing strategy is proposed, by considering the individual energy needs of each residence and their EVs. The strategy is formulated based on the demand of the households and EVs charging. By enabling the fuzzy cognitive maps theory, a graph is designed, aiming to establish the correlation among the individual energy parameters and the characteristics of the renewable energy sources (RES). The optimization results reveal that the adopted hybrid approach can reduce the energy cost significantly, up to almost 40%, while enabling distribution system operators (DSOs) to incorporate additional loads, without the need for network expansion. Finally, based on the extracted results, a short discussion about the concept of EVs' charging by residential RES is presented.

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Addressing Accuracy Issues of Fuzzy Cognitive Map-Based Classifiers

Nápoles,

Jastrzębska

2024

Fuzzy Cognitive Maps

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Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps

Cited by 30 publications

References 39 publications

Probability Density Forecasting of Wind Power Based on Transformer Network with Expectile Regression and Kernel Density Estimation

Probability Density Forecasting of Wind Power Based on Transformer Network with Expectile Regression and Kernel Density Estimation

An Optimal Distributed RES Sizing Strategy in Hybrid Low Voltage Networks Focused on EVs’ Integration

Addressing Accuracy Issues of Fuzzy Cognitive Map-Based Classifiers

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