Prediction of photovoltaic power output for microgrids using back-propagation neural network

Wei-Tzer Huang, Kai-chao Yao, Hao Chuan Luo, Hong Ting Chen, Yung Ruei Chang, Yih Der Lee, Yuan Hsiang Ho

Research output: Contribution to journalArticle


The main purpose of this study is to predict photovoltaic (PV) power output using back-propagation neural network (BPNN). The mean relative error is used to evaluate the prediction accuracy. This study used the historical data of temperature, humidity, and PV power output to predict the day-ahead hourly power output. Eventually, the day-ahead hourly prediction results are applied to the energy management system of the microgrids (MGs) of the remote island of Taiwan. Results demonstrate that the proposed BPNN algorithm for prediction is accurate and effective. Outcomes are also beneficial for the day-ahead unit commitment of MGs.

Original languageEnglish
Pages (from-to)211-218
Number of pages8
JournalICIC Express Letters, Part B: Applications
Issue number3
Publication statusPublished - 2019 Mar 1


All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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