Probabilistic power curve estimation based on meteorological factors and density LSTM

Wang, Peng; Li, Yanting; Zhang, Guangyao

doi:10.1016/j.energy.2023.126768

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…In their study, Wang et al [55] emphasized that considering the impact of multivariate historical meteorological factors, including wind speed, wind direction, and ambient temperature, on wind power output helps enhance forecasting performance. Consistent with this study, this paper considered both wind speed and direction.…”

Section: State Of the Artmentioning

confidence: 99%

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Famoso,

Oliveri,

Brusca

et al. 2024

Energies

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This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach.

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Section: State Of the Artmentioning

confidence: 99%

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Famoso,

Oliveri,

Brusca

et al. 2024

Energies

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“…Existing studies [9][10][11][12] have shown that the climate has obvious geographical characteristics at different geographical scales, such as the south and north of China, and different environments have diverse effects on the operational statuses of wind turbines. According to the existing research [13][14][15][16], the randomness and uncertainty of wind volume have a large impact on the volatility of wind power generation, and wind speed transmission is different at different heights such as 20 m, 50 m, 80 m, and 100 m. Researchers have proposed algorithms that consider the effect of wind speed on the operating states of wind turbines to solve the problem of the accuracy of wind speed prediction.…”

Section: Previous Related Workmentioning

confidence: 99%

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Gou,

Zhang,

Wen

et al. 2024

Sustainability

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The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that the root mean square error value (hereinafter abbreviated as RMSE) and the mean absolute error value (hereinafter abbreviated as MAPE) of the improved XGBoost–LSTM algorithm are decreased compared with those for the LSTM and XGBoost algorithms, among which the RMSE is reduced by 8.26% and 4.15% and the MAPE is reduced by 24.56% and 27.99%, respectively; its goodness-of-fit R2 value is closer to 1. This indicates that the algorithm proposed in this paper reduces the existing defects present in some current algorithms, and the prediction accuracy is effectively improved, which is of great value in improving the reliability of the system.

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“…To avoid the above situation, this paper proposes a KDE-based weather factor partitioning method to classify each weather factor into several grades. Firstly, KDE [35][36][37][38] was carried out for eight meteorological factors F, T, and S, respectively, and the probability density curve of the annual distribution of each meteorological factor was obtained. Then, the quantile was selected according to the probability distribution characteristics of the curve.…”

Section: Variable Quantile Division Considering the Distribution Char...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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Probabilistic power curve estimation based on meteorological factors and density LSTM

Cited by 11 publications

References 37 publications

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

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

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