Frontiers of Data and Domputing ›› 2021, Vol. 3 ›› Issue (5): 109-117.doi: 10.11871/jfdc.issn.2096-742X.2021.05.008

• Technology and Applicaton • Previous Articles     Next Articles

Photovoltaic Power Forecast Based on Time-division LSTM

XU Ke1,2(),WANG Jue1,*(),YAO Tiechui1,2(),LI Kai1()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2021-03-16 Online:2021-10-20 Published:2021-11-24
  • Contact: WANG Jue E-mail:xuke@cnic.cn;wangjue@sccas.cn;yaotiechui@cnic.cn;kai.li@cnic.cn

Abstract:

[Objective] An accurate forecast of photovoltaic (PV) power has great significance for power dispatch. [Methods] PV generation is a continuous process. The variation of PV power at each moment is affected by meteorological factors at both current and historical moments. For the time correlation of PV power, we propose a PV power forecasting model based on time-division long short-term memory (LSTM) in this paper. The Pearson correlation coefficient is used to study the series characteristics and meteorological characteristics of PV power. And the meteorological characteristics highly related to the PV power at historical moments are selected as the input of the PV power forecasting model. [Results] Based on the real-world datasets, the PV power forecasting model based on time-division LSTM can adapt to the PV series time misalignment and therefore achieve a high precision rate. [Conclusions] The proposed model has lower forecasting error than the traditional LSTM model.

Key words: time-division LSTM, Pearson correlation coefficient, photovoltaic power forecast