基于k-sums分段聚类的动态组合学习光伏短期功率预测

doi:10.16180/j.cnki.issn1007-7820.2024.04.010

Abstract

Abstract:

At present, the prediction accuracy of a single model is difficult to remain optimal with power fluctuation. To improve the stability of grid connected system operation and energy saving dispatching of power grid, this study proposes a dynamic learning combination short-term power prediction method based on k-sums hierarchical clustering. The weather types are divided into sunny day A₁, cloudy day A₂, and rainy day B through segmentation clustering using k-sums algorithm. The TCN (Temporal Convolutional Network) is used to extract the time series characteristics of data, and the GRU(Gate Recurrent Unit) structure of the fusion extraction time series characteristics module is established with GRU to achieve the effect of being sensitive to the time series characteristics. After dynamically combining the improved GRU structure with the SVM(Support Vector Machine), the Elastic Net algorithm is adopted to output the best weight value to obtain the final prediction value. The power data of photovoltaic power generation and corresponding meteorological data of a region in Jiangsu are used to verify the proposed method. The results show that the MAE(Mean Absolute Error) of the dynamic combination learning model is 1.888, and the RMSE(Root Mean Squared Error) is 2.403.

Key words: k-sums, hierarchical clustering, TCN, improve GRU, SVM, dynamic combination learning, Elastic Net, PV short-term power prediction

CLC Number:

TP183

WU Jiabao, ZENG Guohui, ZHANG Zhenhua. Short-Term Photovoltaic Power Prediction Based on k-sums Quadratic Clustering and Dynamic Combination Learning[J].Electronic Science and Technology, 2024, 37(4): 69-76.

Figures/Tables 9

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References 20

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	晴天	多云	阴雨天
实际功率	1.000	1.000	1.000
总辐照度	0.953	0.937	0.892
法相直射辐照度	0.799	0.791	0.737
水平面散射辐照度	0.601	0.792	0.917
气温	0.194	0.258	0.213
气压	-0.031	-0.146	-0.198
相对湿度	-0.259	-0.211	-0.044

改进GRU	RMSE	MAE
2-1(64)	2.325	1.646
2-2(64,32)	2.112	1.478
2-3(64,32,16)	1.534	1.222
2-3(64,32,32)	1.797	1.740
3-3(64,32,16)	1.787	1.315
4-3(64,32,16)	1.996	1.483

天气	模型	RMSE	MAE
晴天	SVM	2.804	2.711
晴天	k-sums-SVM	2.427	2.376
多云	SVM	3.603	2.834
多云	k-sums-SVM	3.002	2.607
阴雨天	SVM	4.227	3.577
阴雨天	k-sums-SVM	3.988	3.113

	评价指标	GRU	SVM	改进 GRU	动态组合学习
晴天	RMSE	2.279	2.403	1.612	1.314
晴天	MAE	2.047	2.311	1.502	1.157
多云	RMSE	4.221	4.866	3.171	2.644
多云	MAE	3.713	4.153	2.247	1.746
阴雨天	RMSE	5.213	5.671	3.723	3.251
阴雨天	MAE	4.282	4.464	3.277	2.762
平均	RMSE	3.904	4.313	2.835	2.403
平均	MAE	3.347	3.643	2.342	1.888

Short-Term Photovoltaic Power Prediction Based on k-sums Quadratic Clustering and Dynamic Combination Learning

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