An Improved Pattern Sequence-Based Energy Load Forecast Algorithm Based on Self-Organizing Maps and Artificial Neural Networks
TL;DR: In this article , a genetic algorithm was used 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.
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Abstract: 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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Citations
Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption
R. Pérez-Chacón,Gualberto Asencio-Cortés,F. Martínez-Álvarez,A. Troncoso +3 more
TL;DR: This study proposes MV-bigPSF, a novel algorithm for multivariate big data time series forecasting, utilizing pattern sequence recognition and distributed processing to enhance efficiency and accuracy, outperforming univariate and multivariate methods in electricity consumption forecasting.
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