Comparison of Electricity Usage Forecasting Model Evaluation Based on Historical Load Dataset Duration Using Long Short-Term Memory Architecture

Nur Shakirah Md Salleh*, Azizah Suliman, Bo Nørregaard Jørgensen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Electricity prediction helps electric power companies to generate sufficient electrical power to consumers. The primary source used in performing forecasting is historical electricity usage. This research identified the optimum historical load data period in generating the best model for short-term forecasting of a household. The experiment applied Long Short-Term Memory (LSTM) architecture using Adaptive Learning Rate Method (Adadelta) on four categories of dataset: one-year, two-years, three-years, and four-years. The models produced were evaluated using mean squared error (MSE) and mean absolute error (MAE). The model generated from two-years of historical data performed the best among all other models with MSE value of 0.133621 and MAE value of 0.050653. The experiment was enclosed with the application of the model to predict the electricity usage of the following year, shown in two sample categories: one day and one week. Then, the prediction results were compared with the actual load.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Computational Science and Technology - ICCST 2021
EditorsRayner Alfred, Yuto Lim
PublisherSpringer
Publication date2022
Pages675-686
ISBN (Print)9789811685149
ISBN (Electronic)978-981-16-8515-6
DOIs
Publication statusPublished - 2022
Event8th International Conference on Computational Science and Technology, ICCST 2021 - Virtual, Online
Duration: 28. Aug 202129. Aug 2021

Conference

Conference8th International Conference on Computational Science and Technology, ICCST 2021
CityVirtual, Online
Period28/08/202129/08/2021
SeriesLecture Notes in Electrical Engineering
Volume835
ISSN1876-1100

Keywords

  • Electricity
  • LSTM
  • Machine learning
  • Prediction

Fingerprint

Dive into the research topics of 'Comparison of Electricity Usage Forecasting Model Evaluation Based on Historical Load Dataset Duration Using Long Short-Term Memory Architecture'. Together they form a unique fingerprint.

Cite this