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All Journal Frontier Energy System and Power Engineering
Akhmad Fanny Fadhilla
Universitas Negeri Malang
Control & Systems Engineering Electrical & Electronics Engineering Energy

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Journal : Frontier Energy System and Power Engineering

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Gated Recurrent Unit (GRU) for Forecasting Hourly Energy Fluctuations Aji Prasetya Wibawa; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Agung Bella Putra Utama; Leonel Hernandez
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p16-25

Abstract

In the current digital era, energy use undeniably supports economic growth, increases social welfare, and encourages technological progress. Energy-related information is often presented in complex time series data, such as energy consumption data per hour or in seasonal patterns. Deep learning models are used to analyze the data. The right choice of normalization method has great potential to improve the performance of deep learning models significantly. Deep learning models generally use several normalization methods, including min-max and z-score. In this research, the deep learning model chosen is Gated Recurrent Unit (GRU) because the computational load on GRU is lighter, so it doesn't require too much memory. In addition, the GRU data is easier to train, so that it can save training time. This research phase adopts the CRISP-DM methodology in data mining as a solution commonly used in business and research. This methodology involves six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. In this research, the model was obtained using five attribute selection, which applied 2 normalization methods: min-max and z-score. With this normalization, the GRU model produces the best MAPE of 3.9331%, RMSE of 0.9022, and R2 of 0.9022. However, when using z-score normalization, the model performance decreases with MAPE of 10.4332%, RMSE of 0.7602, and R2 of 0.4213. Overall, min-max normalization provides better performance in multivariate time series data analysis.
Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN) Aji Prasetya Wibawa; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Agung Bella Putra Utama; Jehad A.H. Hammad
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p50-57

Abstract

Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In testing with z-score normalization, it showed a performance of MAPE of 10.6120%, RMSE of 0.7648, and R2 of 0.4142.
Co-Authors Ade Kurnia Ganesh Akbari Agung Bella Putra Utama Aji Prasetya Wibawa Alfiansyah Putra Pertama Triono Andien Khansa’a Iffat Paramarta Faradini Usha Setyaputri Jehad A.H. Hammad Leonel Hernandez