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Bidirectional Long Short-Term Memory (Bi-LSTM) Hourly Energy Forecasting

Authors :
Wibawa Aji Prasetya
Fadhilla Akhmad Fanny
Iffat Paramarta Andien Khansa’a
Triono Alfiansyah Putra Pertama
Setyaputri Faradini Usha
Akbari Ade Kurnia Ganesh
Utama Agung Bella Putra
Source :
E3S Web of Conferences, Vol 501, p 01023 (2024)
Publication Year :
2024
Publisher :
EDP Sciences, 2024.

Abstract

The growing demand for energy, especially in urban and densely populated areas, has driven the need for smarter and more efficient approaches to energy resource management. One of the main challenges in energy management is fluctuations in energy demand and production. To overcome this challenge, accurate and careful forecasting of hourly energy fluctuations is required. One method that has proven effective in time series forecasting is using deep learning. The research phase uses the CRISP-DM data mining methodology as a common problem solver for business and research. The scenarios tested in the study used 5 attribute selection scenarios based on correlation values based on target attributes and 2 normalization scenarios. Then, the deep learning model used is Bi-LSTM with hyperparameter tuning grid search. Performance measurement evaluation is performed with MAPE, RMSE, and R2. Based on the tests conducted, it was found that the Bi-LSTM model produced the best MAPE of 7.7256%. RMSE of 0.1234. and R2 of 0.6151 at min-max normalization. In comparison, the results on the z-score normalization are lower with the best MAPE value produced at 10.5525%. RMSE of 0.7627. and R2 of 0.4186.

Subjects

Subjects :
Environmental sciences
GE1-350

Details

Language :
English, French
ISSN :
22671242
Volume :
501
Database :
Directory of Open Access Journals
Journal :
E3S Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.5f4ae765060b40bfb6462ce41a135e34
Document Type :
article
Full Text :
https://doi.org/10.1051/e3sconf/202450101023