Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression
Solar radiation intensity is intermittent and uncertain under the influence of meteorological conditions. Clustering them and obtaining high-precision and reliable probabilistic forecasting results play a vital role in the planning and management of solar power. In this study, a novel K-means time s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9422819/ |
| _version_ | 1818589186272264192 |
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| author | Zhendong Zhang Chao Wang Xiaosheng Peng Hui Qin Hao Lv Jialong Fu Hongyu Wang |
| author_facet | Zhendong Zhang Chao Wang Xiaosheng Peng Hui Qin Hao Lv Jialong Fu Hongyu Wang |
| author_sort | Zhendong Zhang |
| collection | DOAJ |
| description | Solar radiation intensity is intermittent and uncertain under the influence of meteorological conditions. Clustering them and obtaining high-precision and reliable probabilistic forecasting results play a vital role in the planning and management of solar power. In this study, a novel K-means time series clustering (K-MTSC) algorithm is first proposed to cluster solar radiation intensity and compared with astronomy method and K-means. Then, different feature inputs for different categories of solar radiation intensity are screened. Afterwards, the different kernel functions of Gaussian process regression (GPR) are compared and optimal kernel function is selected in terms of deterministic forecasting and probabilistic forecasting for different categories. Finally, the case study in Tibet province, China are performed to verify the validity and practicability of this research model and method. In this experiment, the average accuracy of GPR is 44% higher than that of Artificial Neural Network ANN, and 17% higher than that of Support Vector Regression. The experiments show that (1) the clustering results obtained by the K-MTSC algorithm have a larger inter-group distance and a smaller intra-group distance, and at the same time, it will not destroy the continuity of the time series. (2) The probability forecast results obtained by GPR are reliable and high-accuracy. |
| first_indexed | 2024-12-16T09:36:39Z |
| format | Article |
| id | doaj.art-31be382d737f42b0886669d65ba82d45 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T09:36:39Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31be382d737f42b0886669d65ba82d452022-12-21T22:36:23ZengIEEEIEEE Access2169-35362021-01-019890798909210.1109/ACCESS.2021.30774759422819Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process RegressionZhendong Zhang0https://orcid.org/0000-0001-8276-6488Chao Wang1https://orcid.org/0000-0002-3123-0170Xiaosheng Peng2https://orcid.org/0000-0002-9958-7045Hui Qin3https://orcid.org/0000-0002-8805-0015Hao Lv4Jialong Fu5https://orcid.org/0000-0002-8901-8500Hongyu Wang6School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, ChinaSchool of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaSolar radiation intensity is intermittent and uncertain under the influence of meteorological conditions. Clustering them and obtaining high-precision and reliable probabilistic forecasting results play a vital role in the planning and management of solar power. In this study, a novel K-means time series clustering (K-MTSC) algorithm is first proposed to cluster solar radiation intensity and compared with astronomy method and K-means. Then, different feature inputs for different categories of solar radiation intensity are screened. Afterwards, the different kernel functions of Gaussian process regression (GPR) are compared and optimal kernel function is selected in terms of deterministic forecasting and probabilistic forecasting for different categories. Finally, the case study in Tibet province, China are performed to verify the validity and practicability of this research model and method. In this experiment, the average accuracy of GPR is 44% higher than that of Artificial Neural Network ANN, and 17% higher than that of Support Vector Regression. The experiments show that (1) the clustering results obtained by the K-MTSC algorithm have a larger inter-group distance and a smaller intra-group distance, and at the same time, it will not destroy the continuity of the time series. (2) The probability forecast results obtained by GPR are reliable and high-accuracy.https://ieeexplore.ieee.org/document/9422819/Solar radiation intensityK-means time series clusteringprobabilistic forecastingGaussian process regression |
| spellingShingle | Zhendong Zhang Chao Wang Xiaosheng Peng Hui Qin Hao Lv Jialong Fu Hongyu Wang Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression IEEE Access Solar radiation intensity K-means time series clustering probabilistic forecasting Gaussian process regression |
| title | Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression |
| title_full | Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression |
| title_fullStr | Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression |
| title_full_unstemmed | Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression |
| title_short | Solar Radiation Intensity Probabilistic Forecasting Based on K-Means Time Series Clustering and Gaussian Process Regression |
| title_sort | solar radiation intensity probabilistic forecasting based on k means time series clustering and gaussian process regression |
| topic | Solar radiation intensity K-means time series clustering probabilistic forecasting Gaussian process regression |
| url | https://ieeexplore.ieee.org/document/9422819/ |
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