基于复值自编码器和递归神经网络的太阳能预测

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS
Aymen Rhouma, Yahia Said
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引用次数: 0

摘要

可再生能源正在成为一种值得信赖的能源。能源预测是一个重要的研究领域,它用于提供有关可再生能源发电厂未来发电量的信息。能源预测有助于通过最小化能源生产的运营成本来安全管理电网。近年来,基于深度学习技术的能源预测取得了巨大的成功,但目前取得的结果与目标结果还相差甚远。普通的深度学习模型已用于时间序列处理。本文将复值自编码器与LSTM神经网络相结合,用于太阳能预测。采用复值自编码器对时间序列进行处理,具有处理输入参数较多的复杂数据的优点。能量值作为实值,气象条件作为虚值。考虑天气状况有助于更好地预测发电量。在Fingrid开放数据集上对该方法进行了评估。用平均绝对误差(MAE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)来评价所提出方法的性能。通过对比研究证明了该方法的有效性。报告的结果表明了所提出方法的有效性。关键词:太阳能预测;人工智能;复数autoencoder;长短期记忆;深的
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solar Energy Forecasting Based on Complex Valued Auto-encoder and Recurrent Neural Network
Renewable energy is becoming a trusted power source. Energy forecasting is an important research field, which is used to provide information about the future power generation of renewable energy plants. Energy forecasting helps to safely manage the power grid by minimizing the operational cost of energy production. Recent advances in energy forecasting based on deep learning techniques have shown great success but the achieved results still too far from the target results. Ordinary deep learning models have been used for time series processing. In this paper, a complex-valued autoencoder was coupled with an LSTM neural network for solar energy forecasting. The complex-valued autoencoder was used to process the time series with the advantage of processing more complex data with more input arguments. The energy value was used as a real value and the weather condition was considered as the imaginary value. Taking into account the weather condition helps to better predict power generation. The proposed approach was evaluated on the Fingrid open data dataset. The mean absolute error (MAE), rootmean-square error (RMSE) and mean absolute percentage error (MAPE) was used to evaluate the performance of the proposed method. A comparison study was performed to prove the efficiency of the proposed approach. Reported results have shown the efficiency of the proposed approach. Keywords—Solar energy forecasting; artificial intelligence; complex-valued autoencoder; long-short term memory; deep
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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