VoltCast: A Medium-term Multivariate Forecasting Web App for Electricity Demand, Price, and Supply Using Deep Learning

DOI

10.2991/978-94-6463-684-0_8How to use a DOI?

Keywords

electricity forecasting; medium-term; multivariate; demand; load; price; supply; deep learning; GRU; LSTM; TCN

Abstract

Forecasts of electricity are crucial for providers and beneficial for consumers, aiding in the planning and management of electric use and grid operations. Time series forecasting has been a vital tool in this field, with various methods and architectures emerging over the decades. With the availability of large data, deep learning approaches have become more plausible and preferable for forecasting tasks. This study developed a web application utilizing deep learning to forecast electricity demand, price, and supply up to 180 days ahead using data from the Philippines. It compared GRU, LSTM, and TCN algorithms using MSE, RMSE, MAE, and MAPE as error metrics, with ARIMA models as benchmarks. The results showed that deep learning models outperformed ARIMA models. Moreover, LSTM performed best for Visayas supply, while GRU performed best in forecasting demand, price, and Luzon supply. The best models were integrated into the web application.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Angelo Malonzo
AU  - Perlita Gasmen
AU  - John Riz Bagnol
PY  - 2025
DA  - 2025/04/30
TI  - VoltCast: A Medium-term Multivariate Forecasting Web App for Electricity Demand, Price, and Supply Using Deep Learning
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2024)
PB  - Atlantis Press
SP  - 118
EP  - 131
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-684-0_8
DO  - 10.2991/978-94-6463-684-0_8
ID  - Malonzo2025
ER  -