Interval Prediction Method for Solar Radiation Based on Kernel Density Estimation and Machine Learning

Zhao, Mengnan; Zhang, Yuhu; Hu, Tao; Wang, Peng

doi:10.1155/2022/7495651

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…Interval prediction results can be used to quantify the uncertainty of monthly runoff, providing a reasonable range of monthly runoff fluctuations and comprehensive information for monthly runoff predictions [64,65]. In this paper, interval forecasting is carried out on the basis of point forecasting.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River

Xue

Dong³

et al. 2023

Sustainability

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River runoff simulation and prediction are important for controlling the water volume and ensuring the optimal allocation of water resources in river basins. However, the instability of medium- and long-term runoff series increases the difficulty of runoff forecasting work. In order to improve the prediction accuracy, this research establishes a hybrid deep learning model framework based on variational mode decomposition (VMD), the mutual information method (MI), and a long short-term memory network (LSTM), namely, VMD-LSTM. First, the original runoff data are decomposed into a number of intrinsic mode functions (IMFs) using VMD. Then, for each IMF, a long short-term memory (LSTM) network is applied to establish the prediction model, and the MI method is used to determine the data input lag time. Finally, the prediction results of each subsequence are reconstructed to obtain the final forecast result. We explored the predictive performance of the model with regard to monthly runoff in the upper Heihe River Basin, China, and compared its performance with other single and hybrid models. The results show that the proposed model has obvious advantages in terms of the performance of point prediction and interval prediction compared to several comparative models. The Nash–Sutcliffe efficiency coefficient (NSE) of the prediction results reached 0.96, and the coverage of the interval prediction reached 0.967 and 0.908 at 95% and 90% confidence intervals, respectively. Therefore, the proposed model is feasible for simulating the monthly runoff of this watershed.

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Section: Discussionmentioning

confidence: 99%

A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River

Xue

Dong³

et al. 2023

Sustainability

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“…The model proposed in this paper can provide reliable solar radiation interval prediction results, which is of great significance to solar energy and many other fields. Solar radiation is the main source of solar power generation, and the reliability and sustainability of solar power generation can also be improved by accurately predicting solar radiation, thus contributing to the development of the solar energy field [37]. In addition, information on solar radiation can also be used for space weather prediction and satellite communications, thereby improving the reliability and safety of space technology.…”

Section: Practical Significance and Practical Value Of The Modelmentioning

confidence: 99%

Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Lyu,

Shen,

Zhao

et al. 2023

Sustainability

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Solar energy, as a clean energy source, has tremendous potential for utilization. The advancement of solar energy utilization technology has led to an increasing demand for solar energy, resulting in a growing need for the accurate prediction of solar radiation. The main objective of this study is to develop a novel model for predicting solar radiation intervals, in order to obtain accurate and high-quality predictions. In this study, the daily sunshine duration (SD), average relative humidity (RHU), and daily average temperature (AT) were selected as the indicators affecting the daily global solar radiation (DGSR). The empirical study conducted in this research utilized daily solar radiation data and daily meteorological data collected at the Hami station in Xinjiang from January 2009 to December 2016. In this study, a novel solar radiation interval prediction model was developed based on the concept of “point prediction + interval prediction”. The Conformer model was employed for the point prediction of solar radiation, while the Generalized Laplace (GLaplace) distribution was chosen as the prior distribution to account for the prediction error. Furthermore, the Solar DeepAR Forecasting (SDAR) model was utilized to estimate parameters of the fitted residual distribution and achieve the interval prediction of solar radiation. The results showed that both models performed well, with the Conformer model achieving a Mean Squared Error (MSE) of 0.8645, a Mean Absolute Error (MAE) of 0.7033 and the fitting coefficient R2 of 0.7751, while the SDAR model demonstrated a Coverage Width-based Criterion (CWC) value of 0.068. Compared to other conventional interval prediction methods, our study’s model exhibited superior accuracy and provided a more reliable solar radiation prediction interval, offering valuable information for ensuring power system safety and stability.

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Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation

Wang,

Chau

et al. 2023

Earth Sci Inform

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Interval Prediction Method for Solar Radiation Based on Kernel Density Estimation and Machine Learning

Cited by 3 publications

References 52 publications

A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River

A Hybrid Forecasting Model to Simulate the Runoff of the Upper Heihe River

Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model

Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation

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