Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study

Zeng, Peng; Sun, Xun; Farnham, David

doi:10.1038/s41598-020-65281-w

Cited by 16 publications

(6 citation statements)

References 36 publications

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“…Moreover, fossil fuels are non-renewable, so they are bound to deplete in the near future. On the other hand, solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy 5 , 6 , thus helping in reducing carbon emissions 7 , 8 . Many countries with great technology advancement have already taken the initiative to develop technologies and machines that could harness energy from the sun.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Heng

Ridwan

Kumar

et al. 2022

Sci Rep

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Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg–Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Heng

Ridwan

Kumar

et al. 2022

Sci Rep

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“…Yet, the reliability of wind farms is impacted by the intermittent and variable nature of wind energy (Albalawi et al, 2022;Chen et al, 2022). Therefore, it is becoming increasingly important that wind energy be predictable (Zeng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms

et al. 2023

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Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast wind speed accurately. However, the accuracy of these models still needs more improvements to achieve more accurate results. In this paper, an optimized model is proposed for boosting the accuracy of the prediction accuracy of wind speed. The optimization is performed in terms of a new optimization algorithm based on dipper-throated optimization (DTO) and genetic algorithm (GA), which is referred to as (GADTO). The proposed optimization algorithm is used to optimize the bidrectional long short-term memory (BiLSTM) forecasting model parameters. To verify the effectiveness of the proposed methodology, a benchmark dataset freely available on Kaggle is employed in the conducted experiments. The dataset is first preprocessed to be prepared for further processing. In addition, feature selection is applied to select the significant features in the dataset using the binary version of the proposed GADTO algorithm. The selected features are utilized to learn the optimization algorithm to select the best configuration of the BiLSTM forecasting model. The optimized BiLSTM is used to predict the future values of the wind speed, and the resulting predictions are analyzed using a set of evaluation criteria. Moreover, a statistical test is performed to study the statistical difference of the proposed approach compared to other approaches in terms of the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. The results of these tests confirmed the proposed approach’s statistical difference and its robustness in forecasting the wind speed with an average root mean square error (RMSE) of 0.00046, which outperforms the performance of the other recent methods.

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“…Some studies have examined the uncertainty of solar and wind power equipped with energy storage to assess their potential to meet future electricity demand 20 . Prediction methods such as linear regression models and eXtreme Gradient Boosting have been utilized to forecast the uncertainty of wind and solar generation in specific regional areas, considering seasonal or yearly analyses 21 , 22 . However, limited research has focused on analyzing the spatiotemporal uncertainty distributions of renewable energy 23 , 24 .…”

Section: Introductionmentioning

confidence: 99%

Inherent spatiotemporal uncertainty of renewable power in China

Wang,

Chen,

Tan

et al. 2023

Nat Commun

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Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested.

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Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study

Cited by 16 publications

References 36 publications

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms

Inherent spatiotemporal uncertainty of renewable power in China

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