An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation

Hui, Huang; Zhu, Qiliang; Xiao-hong, Zhu; Zhang, Jinhua

doi:10.3390/en16041963

Cited by 4 publications

(3 citation statements)

References 49 publications

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“…At present, multi-factor features are effective in improving the prediction accuracy of wind power prediction modeling compared with single-factor features. Huang et al [26] used the Bayesian optimization model to adaptively select the base model by using the coefficient of determination index to tune the hyperparameters, which improved the generalization and prediction accuracy of the prediction model and achieved good prediction results. The spatial-temporal correlation between features in wind power prediction is widespread [27,28].…”

Section: Related Workmentioning

confidence: 99%

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Zhang,

Li,

Cheng

et al. 2024

Energies

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High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions.

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Section: Related Workmentioning

confidence: 99%

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Zhang,

Li,

Cheng

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

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“…It offers several advantages, such as strong interpretability, high algorithm stability, and accuracy [24]. As a result, local and international experts have extensively researched and applied the stacking algorithm in the context of PV power prediction [25][26][27]. In Reference [28], Hongchao Zhang et al proposed multiple stacking models to predict PV power generation using two datasets.…”

Section: Introductionmentioning

confidence: 99%

XGBoost–SFS and Double Nested Stacking Ensemble Model for Photovoltaic Power Forecasting under Variable Weather Conditions

Zhou,

Chen,

et al. 2023

Sustainability

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Sustainability can achieve a balance among economic prosperity, social equity, and environmental protection to ensure the sustainable development and happiness of current and future generations; photovoltaic (PV) power, as a clean, renewable energy, is closely related to sustainability providing a reliable energy supply for sustainable development. To solve the problem with the difficulty of PV power forecasting due to its strong intermittency and volatility, which is influenced by complex and ever-changing natural environmental factors, this paper proposes a PV power forecasting method based on eXtreme gradient boosting (XGBoost)–sequential forward selection (SFS) and a double nested stacking (DNS) ensemble model to improve the stability and accuracy of forecasts. First, this paper analyzes a variety of relevant features affecting PV power forecasting and the correlation between these features and then constructs two features: global horizontal irradiance (GHI) and similar day power. Next, a total of 16 types of PV feature data, such as temperature, azimuth, ground pressure, and PV power data, are preprocessed and the optimal combination of features is selected by establishing an XGBoost–SFS to build a multidimensional climate feature dataset. Then, this paper proposes a DNS ensemble model to improve the stacking forecasting model. Based on the gradient boosting decision tree (GBDT), XGBoost, and support vector regression (SVR), a base stacking ensemble model is set, and a new stacking ensemble model is constructed again with the metamodel of the already constructed stacking ensemble model in order to make the model more robust and reliable. Finally, PV power station data from 2019 are used as an example for validation, and the results show that the forecasting method proposed in this paper can effectively integrate multiple environmental factors affecting PV power forecasting and better model the nonlinear relationships between PV power forecasting and relevant features. This is more applicable in the case of complex and variable environmental climates that have higher forecasting accuracy requirements.

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“…This poses a great challenge to the stability and security of the whole power system [5]. Solar Power Forecasting (SPF) is designed to forecast the SP for a desired future period, which can provide references for dispatch and control in power systems [6].…”

Section: Introductionmentioning

confidence: 99%

Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting

Ren,

Wang,

Cao

et al. 2023

Energies

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A common dilemma with deep-learning-based solar power forecasting models is their heavy dependence on a large amount of training data. Few-Shot Solar Power Forecasting (FSSPF) has been investigated in this paper, which aims to obtain accurate forecasting models with limited training data. Integrating Transfer Learning and Meta-Learning, approaches of Feature Transfer and Rapid Adaptation (FTRA), have been proposed for FSSPF. Specifically, the adopted model will be divided into Transferable learner and Adaptive learner. Using massive training data from source solar plants, Transferable learner and Adaptive learner will be pre-trained through a Transfer Learning and Meta-Learning algorithm, respectively. Ultimately, the parameters of the Adaptive learner will undergo fine-tuning using the limited training data obtained directly from the target solar plant. Three open solar power forecasting datasets (GEFCom2014) were utilized to conduct 24-h-ahead FSSPF experiments. The results illustrate that the proposed FTRA is able to outperform other FSSPF approaches, under various amounts of training data as well as different deep-learning models. Notably, with only 10-day training data, the proposed FTRA can achieve an RMSR of 8.42%, which will be lower than the 0.5% achieved by the state-of-the-art approaches.

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An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation

Cited by 4 publications

References 49 publications

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

XGBoost–SFS and Double Nested Stacking Ensemble Model for Photovoltaic Power Forecasting under Variable Weather Conditions

Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting

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