On AutoMLs for Short-Term Solar Radiation Forecasting in Brazilian Northeast

被引：0
|
作者：
Mendes, Hugo Abreu [1 ]
机构：
[1] Univ Pernambuco POLI, Recife, PE, Brazil
来源：
2021 7TH INTERNATIONAL CONFERENCE ON ENGINEERING AND EMERGING TECHNOLOGIES (ICEET 2021) | 2021年
关键词：
Solar Radiation; Time Series; AutoMls; Machine Learning; Optimization; WIND-SPEED; SERIES; MODEL;
D O I：
10.1109/ICEET53442.2021.9659788
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Machine learning models applied to solar radiation time series helps to improve forecast results. AutoMLs are becoming very popular for industrial application, because it simplifies some the challenge of finding the best suited model. This work presents three different optimizations of known models applied for solar radiation forecasting, ACOLSTM, ACOCLSTM and AGMMFF. A methodology was used to obtain the results, with Brazilian INMET hourly scaled data, which were compared to the open sources AutoMLs TPOT, HPSKLEARN and AutoKeras. The obtained results for the presented models is promising for use in automatic solar radiation forecasting systems since ACOLSTM was found to outperform the compared models.
引用
收藏
页码：335 / 340
页数：6
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