Power Generation Technology ›› 2024, Vol. 45 ›› Issue (4): 684-695.DOI: 10.12096/j.2096-4528.pgt.23045

• New Energy • Previous Articles     Next Articles

A Novel Distributed Photovoltaic Output Interval Prediction Method

Kang YANG1, Lanqing LI1, Yifeng LI1, Dongkuo SONG2, Bolun WANG1, Jin CHEN2, Xia ZHOU3, Yu SHAN3   

  1. 1.State Grid Jiangsu Electric Power Co. , Ltd. , Nanjing 210008, Jiangsu Province, China
    2.Guodian NARI Technology Co. , Ltd. , Nanjing 211000, Jiangsu Province, China
    3.College of Automation/College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, Jiangsu Province, China
  • Received:2023-04-12 Revised:2023-08-25 Published:2024-08-31 Online:2024-08-27
  • Contact: Xia ZHOU
  • Supported by:
    National Natural Science Foundation of China(61933005)

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: