基于深度学习方法的太阳能光伏发电预测

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mu-Yen Chen, Hsiu-Sen Chiang, Chih-Yung Chang
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引用次数: 0

摘要

近年来,可再生能源发电受到越来越多的关注。因为发电量预测有助于合理用电和管理。因此,本研究使用时间序列分析和深度学习方法、长短期记忆(LSTM)、时间卷积网络(TCN)和门控循环单元(GRU)来预测太阳能发电。此外,本研究还采用不同的时间间隔(每十分钟、每三十分钟、每小时、每天)来预测发电量并评估其性能。对比四种深度学习模型,LSTM的预测性能最好,而TCN模型的预测性能较差。此外,时间间隔长度对预测性能影响很大。时间间隔被划分得更小,各种深度学习模型的性能相对较好且稳定;否则,模型的性能很差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solar Photovoltaic Power Generation Prediction based on Deep Learning Methods
In recent years, renewable energy power generation has received more and more attention. Since the forecast of electricity generation is helpful for properly using and managing electricity. Therefore, this study uses time series analysis and deep learning methods, Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Gated Recurrent Unit (GRU), to forecast solar power generation. Furthermore, this study also uses different time intervals (every ten minutes, every thirty minutes, hourly, daily) to forecast the power generation and evaluate their performances. In comparing the four deep learning models, the prediction performance of LSTM is the best, while the performance of the TCN model is poor. In addition, the time interval length greatly influences the prediction performance. The time interval is divided into smaller, and the performance of various deep learning models is relatively good and stable; otherwise, the performance of the models is poor.
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来源期刊
IET Networks
IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
5.00
自引率
0.00%
发文量
41
审稿时长
33 weeks
期刊介绍: IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.
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