Forecasting Tetouan energy demand employing shift approach in machine-learning: complementing econometric insights

Muhammad Tanveer Islam (), Sartaj Aziz Turja (), Md Tawfiqul Islam (), Md Mominur Rahman () and Ahsan Habib ()
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Muhammad Tanveer Islam: Bangladesh Institute of Governance and Management
Sartaj Aziz Turja: Shahjalal University of Science and Technology
Md Tawfiqul Islam: Brunel University London
Md Mominur Rahman: Bangladesh Institute of Governance and Management
Ahsan Habib: Deakin University

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 2, No 34, 1833-1860

Abstract: Abstract GDP growth with sustainable development for a country is highly dependent on power supply and consumption, and in the modern world, human development cannot think without electricity. It is used in every human development process in a particular country. Power consumption is a crying need for economic growth for a growing nation and economy like Morocco. However, producing electricity is costly, and it is necessary to make it practical for future use. Predicting electricity consumption for effective power management is crucial, and many existing research studies have been conducted on the power consumption demand forecast for the Tetouan City of Morocco using the traditional approach. Still, their outputs are not efficient and accurate compared with our approach. Traditional techniques use target variables directly and do not maintain past data trends. Our study solves this by proposing a consumption shift approach where past consumption and other variables form predictor variables to forecast future consumption. In our study, we use our proposed shift approach for the Quads, Boussafou, and Smir power zone data of Tetouan City for 2017 and the combination (average) of these three power zones for 2017. We used two machine-learning models for future consumption prediction: fb-prophet and neural prophet. Our analysis shows that Tetouan City’s power usage forecasts performed better than traditional forecasting. MAPE increased by 2% and $$R^2$$ R 2 by 5% for 10-minute intervals and by 1.5% and $$R^2$$ R 2 by 4.5% for hourly intervals. Compared with the benchmark study on the same dataset, our approach gives 23.33% and 88% better RMSE for 10-minute and hourly interval datasets. Instead of using the machine-learning model for prediction, we use an econometric model (OLS) separately in our study to identify the relationship between power demand and environmental features and observed temperature and wind speed have a positive impact. In contrast, humidity has a negative impact on the power consumption of Tetouan City.

Keywords: FB-prophet; Neural prophet; Shift approach; Traditional approach; Econometric model; Auto-correlation; Environmental variable (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-024-02043-0

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