基于自组织映射和人工神经网络的改进模式序列能源负荷预测算法

IF 3.7 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
D. Criado-Ramón, L. Ruiz, M. Pegalajar
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引用次数: 1

摘要

基于模式序列的模型是一种预测算法,它利用聚类和其他技术比传统的机器学习模型更快地产生易于解释的预测。本研究着重于它们在能源需求预测中的应用,并介绍了该领域的两个重要贡献。首先,本研究评估了基于模式序列的模型在大数据集上的使用。不像以前的工作只使用一个数据集或使用少于两年的数据集的多个数据集,这项工作在三个不同的公共数据集中评估模型,每个数据集包含11年的数据。其次,我们提出了一种新的基于模式序列的算法,该算法使用遗传算法来优化聚类数量以及预测方法的所有其他超参数,而不是使用先前建议中常用的聚类有效性指数(CVIs)。结果表明,神经网络比任何基于模式序列的算法提供更准确的结果,并且我们提出的算法优于其他基于模式序列的算法,尽管需要更长的训练时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks
Pattern sequence-based models are a type of forecasting algorithm that utilizes clustering and other techniques to produce easily interpretable predictions faster than traditional machine learning models. This research focuses on their application in energy demand forecasting and introduces two significant contributions to the field. Firstly, this study evaluates the use of pattern sequence-based models with large datasets. Unlike previous works that use only one dataset or multiple datasets with less than two years of data, this work evaluates the models in three different public datasets, each containing eleven years of data. Secondly, we propose a new pattern sequence-based algorithm that uses a genetic algorithm to optimize the number of clusters alongside all other hyperparameters of the forecasting method, instead of using the Cluster Validity Indices (CVIs) commonly used in previous proposals. The results indicate that neural networks provide more accurate results than any pattern sequence-based algorithm and that our proposed algorithm outperforms other pattern sequence-based algorithms, albeit with a longer training time.
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来源期刊
Big Data and Cognitive Computing
Big Data and Cognitive Computing Business, Management and Accounting-Management Information Systems
CiteScore
7.10
自引率
8.10%
发文量
128
审稿时长
11 weeks
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