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

doi:10.24507/icicelb.10.03.211

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

Department of Industrial Education and Technology

Research output: Contribution to journal › Article › peer-review

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	https://doi.org/10.24507/icicelb.10.03.211
Publication status	Published - 2019 Mar 1

All Science Journal Classification (ASJC) codes

Computer Science(all)

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title = "Prediction of photovoltaic power output for microgrids using back-propagation neural network",

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.",

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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

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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.

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