Priori-guided and data-driven hybrid model for wind power forecasting

Huang, Yu‐Chun; Liu, Shuai; Hu, Wenshan

doi:10.1016/j.isatra.2022.07.028

Cited by 19 publications

(6 citation statements)

References 33 publications

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“…The inland and offshore wind farms' datasets are used to ensure the precision and stability of the model. In Huang et al (2023), a hybrid methodology is implemented that employs the algorithm of fuzzy C-means clustering to identify weather parameters across different places. A three-tiered hierarchical structure has been designed to use both the gathered data and prior knowledge.…”

Section: Related Workmentioning

confidence: 99%

Short-term wind power forecasting using integrated boosting approach

Ahmed,

Muhammad,

Abbas

et al. 2024

Front. Energy Res.

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Rapidly increasing global energy demand and environmental concerns have shifted the attention of policymakers toward the large-scale integration of renewable energy resources (RERs). Wind energy is a type of RERs with vast energy potential and no environmental pollution is associated with it. The sustainable development goals: affordable and clean energy, climate action, and industry, innovation and infrastructure, can be achieved by integrating wind energy into the existing power systems. However, the integration of wind energy will bring instability challenges due to its intermittent nature. Mitigating these challenges necessitates the implementation of effective wind power forecasting models. Therefore, we have proposed a novel integrated approach, Boost-LR, for hour-ahead wind power forecasting. The Boost-LR is a multilevel technique consisting of non-parametric models, extreme gradient boosting (XgBoost), categorical boosting (CatBoost), and random forest (RF), and parametric approach, linear regression (LR). The first layer of the Boost-LR uses the boosting algorithms that process the data according to their tree development architectures and pass their intermediary forecast to LR which is deployed in layer two and processes the intermediary forecasts of layer one models to provide the final predicted wind power. To demonstrate the generalizability and robustness of the proposed study, the performance of Boost-LR is compared with the individual models of CatBoost, XgBoost, RF, deep learning networks: long short-term memory (LSTM) and gated recurrent unit (GRU), Transformer and Informer models using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and normalized root mean square error (NRMSE). Findings demonstrate the effectiveness of the Boost-LR as its forecasting performance is superior to the compared models. The improvement in MAE of Boost-LR is recorded as to be 31.42%, 32.14%, and 27.55% for the datasets of Bruska, Jelinak, and Inland wind farm, respectively as compared to the MAE of CatBoost which is revealed as the second-best performing model. Moreover, the proposed study also reports a literature comparison that further validates the effectiveness of Boost-LR performance for short-term wind power forecasting.

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Section: Related Workmentioning

confidence: 99%

Short-term wind power forecasting using integrated boosting approach

Ahmed,

Muhammad,

Abbas

et al. 2024

Front. Energy Res.

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“…The existing literature has studied the power quality of WHCGS from multiple aspects. [8][9][10][11][12][13][14][15][16][17] When hydropower system (HPS) is mainly involved in the power quality improvement of complementary systems (CSs), Stefanos took a typical island system as an example, and proves that the wind-hydro CS has more advantages than the wind-heat CS in improving the power quality. [8] Guillermo built an ultralong pipeline pressure model to test the governor proportional integral parameters of the hydropower station, thereby improving the frequency and power stability of the island CS.…”

Section: Introductionmentioning

confidence: 99%

“…[ 16 ] Huang et al combined machine learning algorithms to develop a wind power prediction model with high accuracy and reasonableness as a way to mitigate the stochastic volatility of wind energy. [ 17 ] The above literature has explored the effect of various operational characteristics of wind and hydropower on the power quality of WHCGS, but has not taken into account the variation of hydropower and the adverse consequences of hydraulic instability on the stable operation of WHCGS. In order to significantly improve the power quality of WHCGS to meet the development needs of renewable energy, it is important to analyze the different factors affecting the power quality of the systems in a comprehensive and effective way.…”

Section: Introductionmentioning

confidence: 99%

System Power Quality Analysis under Wind–Hydro Complementary Generation Mode

Gao,

Liu,

Sun

et al. 2024

Energy Tech

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The highly random and characteristics of wind power generation challenge the power quality of the wind–hydro complementary generation system (WHCGS). Herein, the transient characteristics of power quality under the complementary generating mode are studied. First, a nonlinear hydropower system (HPS) model is innovatively established considering the dynamic characteristics of hydrounits under part load operation, and then a wind power system model including wind speed model, wind turbine and its controller, generator and converter is established. Finally, the applicability of WHCGS model in power quality evaluation is established and verified by IEEE‐9 node model. Combined with active power, frequency, and voltage power quality indicators, the effects of wind–hydro capacity ratio and voltage sag on the system are quantified. The results show that the increase in wind power penetration will deteriorate the power quality of WHCGS. Moreover, when the wind power capacity reaches 60 MW, it will damage the voltage and frequency stability of HPS. Besides, the larger the voltage sag amplitude, the worse the power quality of WHCGS. The longer the voltage sag lasts, the higher the risk of WHCGS instability. This article provides a technical reference for the safe and stable operation of WHCGS.

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“…The machine learning-based wind speed predictions for k-NN and conditional KDE, Adaboost-PSO-ELM, and enhanced bee swarm optimization (EBSO), to perform parameter optimization for least squares support vector machine (LSSVM) [11,[26][27][28]30,31] models, were proposed to identify meaningful training data to reduce the volume of modeling data and improve the computing efficiency. They have good generalization ability and robustness and can provide more accurate wind power forecasting [32][33][34][35][36][37][38][39][40][41][42][43][44]. In order to comprehensively understand the research trends in short-term wind power prediction technology in the past three years and further develop the direction of future wind power prediction models, constructive suggestions were provided for short-term wind power prediction, in order to better understand and improve the use of AI methods as well as the correlation between the time resolution and the operation level of the prediction model.…”

Section: Introductionmentioning

confidence: 99%

A Review of Modern Wind Power Generation Forecasting Technologies

Tsai

Hong

et al. 2023

Sustainability

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The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power prediction results exhibit large deviations due to the uncertainty of wind power generation. In order to meet the demand for accessing large-scale wind power into the electricity grid and to further improve the accuracy of short-term wind power prediction, it is necessary to develop models for accurate and precise short-term wind power prediction based on advanced algorithms for studying the output power of a wind power generation system. This paper summarizes the contribution of the current advanced wind power forecasting technology and delineates the key advantages and disadvantages of various wind power forecasting models. These models have different forecasting capabilities, update the weights of each model in real time, improve the comprehensive forecasting capability of the model, and have good application prospects in wind power generation forecasting. Furthermore, the case studies and examples in the literature for accurately predicting ultra-short-term and short-term wind power generation with uncertainty and randomness are reviewed and analyzed. Finally, we present prospects for future studies that can serve as useful directions for other researchers planning to conduct similar experiments and investigations.

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Priori-guided and data-driven hybrid model for wind power forecasting

Cited by 19 publications

References 33 publications

Short-term wind power forecasting using integrated boosting approach

Short-term wind power forecasting using integrated boosting approach

System Power Quality Analysis under Wind–Hydro Complementary Generation Mode

A Review of Modern Wind Power Generation Forecasting Technologies

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