Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network

Yanbin Lin, Dongliang Duan, Xueming Hong, Xiang Cheng, Liuqing Yang, Shuguang Cui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To address the serious challenges to traditional power systems caused by high variability of solar production, especially the ramp events, this paper is aimed at predicting solar radiation accurately at a very-short-term scale, i.e. several minutes ahead. Quite different from traditional methods, our designed system conducts time series prediction based upon meteorological data and sky images' features with the Long Short-Term Memory (LSTM) network to learn the long-term dependency. Experiments have been conducted based on sky images' feature extracted by Convolutional Neural Network, meteorological data and solar geometric data with a large number of features collected over a seven-summer period. Experimental results show that the proposed forecasting system outperforms other methods in the literature, such as solar forecasting based upon optical flow tracking, Feedforward Neural Network (FNN) and Support Vector Regression (SVR), in terms of accuracy and robustness.

Original languageEnglish (US)
Title of host publication2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages963-967
Number of pages5
ISBN (Electronic)9781728167824
DOIs
StatePublished - May 2020
Externally publishedYes
Event2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020 - Chengdu, China
Duration: May 28 2020May 31 2020

Publication series

Name2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020

Conference

Conference2020 Asia Energy and Electrical Engineering Symposium, AEEES 2020
CountryChina
CityChengdu
Period5/28/205/31/20

Keywords

  • Long Short-Term Memory (LSTM)
  • photovoltaic power forecasting
  • solar irradiance
  • time series prediction

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