Abstract
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 language | English |
|---|---|
| Pages (from-to) | 211-218 |
| Number of pages | 8 |
| Journal | ICIC Express Letters, Part B: Applications |
| Volume | 10 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2019 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
Cite this
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Prediction of photovoltaic power output for microgrids using back-propagation neural network. / Huang, Wei-Tzer; Yao, Kai-chao; Luo, Hao Chuan; Chen, Hong Ting; Chang, Yung Ruei; Lee, Yih Der; Ho, Yuan Hsiang.
In: ICIC Express Letters, Part B: Applications, Vol. 10, No. 3, 01.03.2019, p. 211-218.Research output: Contribution to journal › Article
TY - JOUR
T1 - Prediction of photovoltaic power output for microgrids using back-propagation neural network
AU - Huang, Wei-Tzer
AU - Yao, Kai-chao
AU - Luo, Hao Chuan
AU - Chen, Hong Ting
AU - Chang, Yung Ruei
AU - Lee, Yih Der
AU - Ho, Yuan Hsiang
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85065242990&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065242990&partnerID=8YFLogxK
U2 - 10.24507/icicelb.10.03.211
DO - 10.24507/icicelb.10.03.211
M3 - Article
AN - SCOPUS:85065242990
VL - 10
SP - 211
EP - 218
JO - ICIC Express Letters, Part B: Applications
JF - ICIC Express Letters, Part B: Applications
SN - 2185-2766
IS - 3
ER -