Open Access

ARTICLE

Machine Learning Empowered Electricity Consumption Prediction

Maissa A. Al Metrik^*, Dhiaa A. Musleh

Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia

* Corresponding Author: Maissa A. Al Metrik. Email:

(This article belongs to the Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)

Computers, Materials & Continua 2022, 72(1), 1427-1444. https://doi.org/10.32604/cmc.2022.025722

Received 02 December 2021; Accepted 11 January 2022; Issue published 24 February 2022

Abstract

Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes a medium-term prediction model that can predict electricity consumption for a given location in Saudi Arabia. Hence, this study implemented a standalone Artificial Neural Network (ANN) model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia. The dataset used in this research is gathered exclusively from the Saudi Electric Company. The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets. The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning. Finally, the standalone model was tested against the bagging ensemble using the optimized ANN. The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model. The results for the proposed model produced 0.9116 Correlation Coefficient (CC), 0.2836 Mean Absolute Percentage Error (MAPE), 0.4578, Root Mean Squared Percentage Error (RMSPE), 0.0298 MAE, and 0.069 Root Mean Squared Error (RMSE), respectively.

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