@inproceedings{8e5a9ae315844628835b459e21443057,
title = "Comparison of Electricity Load Prediction Errors Between Long Short-Term Memory Architecture and Artificial Neural Network on Smart Meter Consumer",
abstract = "Machine learning can perform electricity load prediction on the demand side. This paper compared the electricity prediction errors between two machine learning algorithms: Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) architecture. LSTM can solve the regression problem in time-series. Due to that, this paper applied LSTM. The traditional machine learning approach, ANN, was used to compare the effectiveness of LSTM in performing the time-series prediction. A dataset that consisted of historical electricity consumption data with independent variables was used in this study. The mean squared error (MSE) and mean absolute error (MAE) evaluation metrics were used to evaluate the models. The model generated using LSTM showed the lowest error with MSE value of 0.1238 and MAE value of 0.0388. These results indicated that choosing a suitable machine learning algorithm for the time-series problem could improve the model generated from the training session.",
keywords = "ANN, Electricity load, LSTM, Regression, Time-series",
author = "Salleh, {Nur Shakirah Md} and Azizah Suliman and J{\o}rgensen, {Bo N{\o}rregaard}",
note = "Funding Information: The publication of this paper was funded by URND TNB Seeding Fund: U-TE-RD-20?08. The authors would like to thank the Institute of Informatics and Computing in Energy (IICE), Universiti Tenaga Nasional (UNITEN) for providing a platform to collaborate with the Center for Energy Informatics, Southern Denmark University (SDU). Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 7th International Conference on Advances in Visual Informatics, IVIC 2021 ; Conference date: 23-11-2021 Through 25-11-2021",
year = "2021",
doi = "10.1007/978-3-030-90235-3_52",
language = "English",
isbn = "9783030902346",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "600--609",
editor = "{Badioze Zaman}, Halimah and Smeaton, {Alan F.} and Shih, {Timothy K.} and Sergio Velastin and Tada Terutoshi and J{\o}rgensen, {Bo N{\o}rregaard} and Hazleen Aris and Nazrita Ibrahim",
booktitle = "Advances in Visual Informatics",
address = "Germany",
}