Predictive Modelling for Power Consumption in Tetouan, Morocco Using Machine Leaning Method

Eghaghe, Etinosa (2024) Predictive Modelling for Power Consumption in Tetouan, Morocco Using Machine Leaning Method. Masters thesis, Dublin, National College of Ireland.

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Abstract

As the world’s population continues to grow, the demand for electricity consumption is on the rise, necessitating accurate prediction to meet increasing energy needs. This research uses machine learning techniques to predict power consumption across three zones in Tetouan, Morocco, a city facing fast Urbanization and increasing demands for energy. Accurate power consumption in this region are important for good energy management and planning to help reduce cost and ensure consistent power supply. Focusing on four machine learning models, Decision Trees, Random Forests, Long Short-Term Memory (LSTM) networks, and XGBoost and using historical dataset comprising DateTime, Temperature, Humidity, Wind Speed, General Diffuse Flow, Diffuse Flows, and zone-specific power consumption variables, from the UCI Machine Learning Repository, the objective of this study is to evaluate the predictive accuracy of the these models, identify main predictors of power consumption and assess their computational efficiency. The findings indicate that the XGBoost model provides the highest predictive accuracy followed by the Random Forest model. The LSTM model, effectively capture temporal dependence’s, making it good for sequential predicting. The Decision Tree model serves as baseline with lower performance compare to the other models.

This research contributes to the field of energy management and demonstrates the effectiveness of advanced predictive modeling techniques narrowed to the unique characteristics of Tetouan. The knowledge gained can help in optimizing energy distribution, reducing cost and promoting sustainable development initiatives in Tetouan’s region.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Alam, Naushad UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Energy industries Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HC Economic History and Conditions > Natural resources > Power resources
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	15 Aug 2025 18:12
Last Modified:	15 Aug 2025 18:12
URI:	https://norma.ncirl.ie/id/eprint/8558

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