Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

Ponkumar, G.; Jayaprakash, S.; Karthick, K.

doi:10.3390/en16145459

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…Simple nonlinear regression models include support vector machine (SVM) [80], least squares SVM (LSSVM), support vector regression (SVR) [81,82], ELM [83], kernel ELM (KELM) [23,46], and various types of ANNs such as BPNN [84], radius basis function neural network (RBFNN), multilayer perceptron (MLP), wavelet neural network (WNN), and Elman neural network (ENN) [85]. Tree-based models encompass decision tree (DT), RF [86], gradient boosting decision tree (GBDT), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost) [87], and light gradient boosting machine (LightGBM) [88]. With the advancement of DL technologies, deep neural networks (DNNs), including RNN [89], LSTM [90], BiLSTM [91], GRU [92], BiGRU, DBN, deep ELM (DELM), and Transformer have been widely applied in WSP and WPP due to their outstanding capability in handling complex nonlinear problems.…”

Section: Single Prediction Modelsmentioning

confidence: 99%

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

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Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein.

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Section: Single Prediction Modelsmentioning

confidence: 99%

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Song,

Tan,

Huang

et al. 2024

Energies

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“…However, CGBR is based on gradient boosting, which can reduce the bias error of the model and thus quickly avoid model overfitting. Recently, machine learning methods using gradient boosting, named LightGBM, XGBoost, and CGBR, have been applied to solve several time series problems: particulate matter estimation (Mampitiya et al 2024), oil formation volume forecasting (Kharazi Esfahani et al 2023), wind power prediction (Ponkumar et al 2023), and stock price prediction (Hartanto et al 2023). The main difference among these three models is the use of the tree growth technique.…”

Section: Proposed New Ensemble Modelmentioning

confidence: 99%

Short-duration prediction of urban storm-water levels using the residual-error ensemble correction technique

Guo,

Chen

2024

Journal of Hydroinformatics

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Predicting water levels in urban storm-water sewer systems is a critical study that could provide vital information to help reduce the risk of flooding. This study proposed a new ensemble model based on the integration of a meta-learner model, residual-error corrections, and a multiple-output framework. To achieve the meta-learner model, three multiple-output data-driven-based (MOD) sewer flooding models employing support vector regression (SVR), k-nearest neighbor regression (KNR), and categorical gradient boosting regression (CGBR) techniques were constructed and applied to predict the short-duration evolution of water levels at seven storm-water gauging sites in Taipei city, Taiwan, considering 10-min datasets spanning nearly 6 years (2016–2021). The Bayesian optimization algorithm was utilized in the training phases for all the models to avoid overfitting or underfitting. Enhancing the analysis of feature importance was also conducted to explore model interpretability based on the SHapley Additive exPlanation (SHAP) algorithm. The outputs of storm-water management model (SWMM) were used as benchmark solutions. For the model validation phase, the proposed integrated model improved the lead-time-averaged Nash–Sutcliffe efficiency of single KNR, SVR, and CGBR models by 174.5, 42.4, and 69.4%, respectively, showing that the proposed accurate model could be useful for urban flood warning systems.

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“…The error deviation and system cost lapses are mitigated based on an account study. In (Ponkumar et al, 2023), a short-term forecasting model leveraging machine learning techniques, such as extreme gradient boosting (XgBoost), categorical boosting (CatBoost), and light gradient boosting machine (LGBM), is presented. The metrics, R-squared, mean square error (MSE), and mean absolute error (MAE) are used to gauge the model's efficacy.…”

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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Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis

Cited by 11 publications

References 38 publications

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

Short-duration prediction of urban storm-water levels using the residual-error ensemble correction technique

Short-term wind power forecasting using integrated boosting approach

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