Ensemble wind speed forecasting system based on optimal model adaptive selection strategy: Case study in China

Dong, Yuqi; Li, Jing; Liu, Zhenkun; Niu, Xinsong; Wang, Jianzhou

doi:10.1016/j.seta.2022.102535

Cited by 11 publications

(4 citation statements)

References 51 publications

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“… Bald eagle search Deriving from the intelligent social behavior of bald eagle fishing, individuals are updated by dividing the movement into three phases. VMD-Combined models-Multi-objective bald eagle search 219 Optimal weights for sub-models, which is composed of ENN, ARIMA, LSSVM, GMDH, ELM, BPNN, and LSTM. Bat algorithm Deriving from the echolocation of bats, individuals are updated based on the speed, position, and loudness.…”

Section: State-of-the-art Deterministic Forecasting Methodsmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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Section: State-of-the-art Deterministic Forecasting Methodsmentioning

confidence: 99%

An overview of deterministic and probabilistic forecasting methods of wind energy

Xie

et al. 2023

iScience

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“…Therefore, the analysis and prediction of wind speed have become an urgent task for wind power industries and relevant research works have caught increasing attention for improving wind power utilization (Zhou et al, 2019). Existing wind speed forecasting research can be summarized into four categories: mathematical–physical methods, statistical methods, machine learning models, and hybrid models (Dong et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

Chen,

Wang,

Zhang

et al. 2024

Journal of Forecasting

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Accurate wind speed prediction is of great importance for the operation of wind farms, and extensive efforts have been made to develop effective forecasting methods in this regard. However, the feature selection of data input as well as optimization of deep learning models have received comparatively less attention, leading to unreliable forecasting results. This research proposes a novel hybrid model that integrates data preprocessing, feature selection, and optimized forecasting for improved wind speed prediction. Specifically, a powerful preprocessing technique is utilized to reduce data noise disturbances, while an innovative two‐stage feature selection is designed to achieve the optimal input data format for forecasting purposes. Moreover, a hybrid forecasting module based on long‐short term memory, which is optimized by the Bayesian optimization algorithm, has been developed to enhance the efficiency and reliability of the model. The empirical study used 10‐min interval wind speed data of four seasons for presentation and evaluation results demonstrated its superior performance in effectively learning the volatility and irregularity features of wind speed series, which established a solid foundation for practical applications in wind power systems.

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“…The transition to renewable energy has become an urgent issue. Clean energy development and utilization are gradually attracting worldwide attention (Dong et al, 2022). As a low-carbon and clean energy source among fossil fuels, natural gas plays a crucial interim role in the transition to a sustainable energy economy (Rabbi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

Duan,

Zhang,

Wang

2023

Front. Energy Res.

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As a clean fossil energy source, natural gas plays a crucial role in the global energy transition. Forecasting natural gas prices is an important area of research. This paper aims at developing a novel hybrid model that contributes to the prediction of natural gas prices. We develop a novel hybrid model that combines the “Decomposition Algorithm” (CEEMDAN), “Ensemble Algorithm” (Bagging), “Optimization Algorithm” (HHO), and “Forecasting model” (SVR). The hybrid model is used for monthly Henry Hub natural gas forecasting. To avoid the problem of data leakage caused by decomposing the whole time series, we propose a rolling decomposition algorithm. In addition, we analyzed the factors affecting Henry Hub natural gas prices for multivariate forecasting. Experimental results indicate that the proposed model is more effective than the traditional model at predicting natural gas prices.

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Ensemble wind speed forecasting system based on optimal model adaptive selection strategy: Case study in China

Cited by 11 publications

References 51 publications

An overview of deterministic and probabilistic forecasting methods of wind energy

An overview of deterministic and probabilistic forecasting methods of wind energy

A novel hybrid forecasting model with feature selection and deep learning for wind speed research

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

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