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(Institut Teknologi Sepuluh Nopember, Indonesia)(2) * Raden Venantius Hari Ginardi
(Institut Teknologi Sepuluh Nopember, Indonesia)(3) Ahmad Saikhu
(Institut Teknologi Sepuluh Nopember, Indonesia)*corresponding author
AbstractEnergy consumption is increasing due to population growth and industrial activity, making electricity essential in human life. With limited natural resources, effective management of electrical resources is crucial to reduce energy usage amidst rising demand. The current trends on using deep learning as prediction can enhance the performances. To have good performance it needs correct preprocessing data, so it will produce a model with less overfitting. This research proposes a model using time-series cross-validation as the splitting data and correlation to choose the best features set for the prediction of electricity consumption. Experiments will compare time-series cross-validation and holdout methods to see the performances of splitting data and enhancing the multi-horizon data. The experiment used 8 sets of feature lists, which are paired in combination based on correlation to ensure the best features that are related. The result is splitting data using time-series cross-validation can maintain good perfomances on mode and holdout can maintain a good evaluation performance across the horizon. Feature sets that include temporal features have excellent results, especially when combined with features that have the strongest correlation relationship with electricity consumption, leading to an enhanced R2. Among all the models tested, CNN-GRU had the best model for multistep prediction across various every horizons and feature sets. KeywordsEnergy Prediction; Deep Learning; Splitting Data; Multistep
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DOIhttps://doi.org/10.29099/ijair.v8i2.1204 |
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