Electrical Energy Consumption Forecasting Based on Conventional and Artificial Intelligence Methods: A Comparison

Authors

Haya H. Binsalim, Effat UniversityFollow
Jana Kamal, Effat UniversityFollow
Salma Badaam, Effat UniversityFollow
Danah Milyani, Effat UniversityFollow
Ghadah S. Alyami, Effat UniversityFollow
Aziza I. HusseinFollow

Abstract

Artificial Intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. The purpose of this paper is to review and compare the classical techniques and the emerging new AI-based techniques used for forecasting electrical energy consumption in buildings. The findings revealed that the Artificial Neural Network (ANN) model achieved the lowest Mean Absolute Percentage Error (MAPE) of 0.928%. AI-based techniques have many advantages over classical techniques, such as their ability to handle a large amount of data and provide accurate and fast results.

Recommended Citation

Binsalim, Haya H.; Kamal, Jana; Badaam, Salma; Milyani, Danah; Alyami, Ghadah S.; and Hussein, Aziza I. (2023) "Electrical Energy Consumption Forecasting Based on Conventional and Artificial Intelligence Methods: A Comparison," Effat Undergraduate Research Journal: Vol. 4: Iss. 1, Article 4.
Available at: https://digitalcommons.aaru.edu.jo/eurj/vol4/iss1/4