A Novel Distributed Photovoltaic Output Interval Prediction Method

doi:10.12096/j.2096-4528.pgt.23045

Abstract

Abstract:

Objectives Distributed photovoltaic power prediction is of great significance for the operation and scheduling of photovoltaic power plants. Point prediction methods are difficult to comprehensively describe the uncertainty of distributed photovoltaic power. This article proposed a distributed photovoltaic power interval prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sparrow search algorithm optimized least squares support vector machine (SSA-LSSVM). Methods Firstly, the photovoltaic sequence was broken down into multimodal components through CEEMDAN, and then the high-frequency non-stationary components obtained from the first decomposition were decomposed twice. Secondly, sample entropy (SE) was used to reconstruct all components into trend and oscillation components. Then, the point prediction values of the two components were obtained through SSA-LSSVM. Finally, the probability density estimation was performed on the point prediction error of the oscillation component, and the stacked point prediction value was used to obtain the overall prediction interval result. Results The interval prediction model proposed in this paper has higher interval coverage and narrower average interval width. Conclusions Adding secondary modal decomposition to distributed photovoltaic power data processing and combining sample entropy to reconstruct its sub-sequences can effectively reduce the complexity of the original prediction components and improve the accuracy of model prediction.

Key words: distributed photovoltaic, power prediction, kernel density estimation, intervel prediction

CLC Number:

TK 01

Kang YANG, Lanqing LI, Yifeng LI, Dongkuo SONG, Bolun WANG, Jin CHEN, Xia ZHOU, Yu SHAN. A Novel Distributed Photovoltaic Output Interval Prediction Method[J]. Power Generation Technology, 2024, 45(4): 684-695.

Figures/Tables 11

Fig. 1 Flow chart of interval prediction method

Fig. 2 Horizontal and vertical correlation coefficient

Fig. 3 CEEMDAN single decomposition result chart

Fig. 4 VMD secondary decomposition result diagram

Fig. 5 Sample entropy reconstruction results

Fig. 6 Comparison chart of PSO and SSA iteration convergence

Fig. 7 Point prediction results of different models

Fig. 8 Radar chart of various model prediction indicators under different test sets

Fig. 9 Prediction results of photovoltaic power interval under two confidence levels

Tab.1 Comparison of interval prediction indicators using different methods

算法	置信度/%	$ε P I C P$ /%	$ε P I N A W$
Bootstrap	85	82.93	1.33
Bootstrap	95	87.80	1.71
蒙特卡洛法	85	85.36	1.231
蒙特卡洛法	95	90.24	1.667
本文方法	85	87.80	1.092 8
本文方法	95	95.12	1.554 0

Tab.1 Comparison of interval prediction indicators using different methods

算法	置信度/%	$ε P I C P$ /%	$ε P I N A W$
Bootstrap	85	82.93	1.33
Bootstrap	95	87.80	1.71
蒙特卡洛法	85	85.36	1.231
蒙特卡洛法	95	90.24	1.667
本文方法	85	87.80	1.092 8
本文方法	95	95.12	1.554 0

Fig. 10 Comparison of interval prediction indicators for different algorithms

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