Publication: Comparison of Electricity Usage Forecasting Model Evaluation Based on Historical Load Dataset Duration Using Long Short-Term Memory Architecture
Date
2022
Authors
Salleh N.S.M.
Suliman A.
J�rgensen B.N.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
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. � 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Description
Brain; Electric power utilization; Electric utilities; Forecasting; Learning algorithms; Mean square error; Memory architecture; Electric power company; Electrical power; Electricity usage; Error values; Forecasting models; Load data; Mean absolute error; Mean squared error; Model evaluation; Primary sources; Long short-term memory