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Aqil, Mounaim
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Computer Science & IT Electrical & Electronics Engineering

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Load forecasting using fuzzy logic, artificial neural network, and adaptive neuro-fuzzy inference system approaches: application to South-Western Morocco Stitou, Hicham; Atillah, Mohamed amine; Boudaoud, Abdelghani; Aqil, Mounaim
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7067-7079

Abstract

The demand for energy on a global scale is continuously rising due to the expansion of energy infrastructure and the increasing number of new appliances. To address this growing need, an efficient energy management system (EMS) has become indispensable. By implementing EMS, both residential and commercial buildings can significantly improve their energy efficiency and consumption. One crucial aspect of enabling EMS to operate efficiently is load forecasting. The accuracy of load forecasting depends on numerous factors. A reliable load forecast model should consider the region’s weather forecast, as it plays a crucial role in developing an accurate prediction. This study is about the medium-term load forecasting (MTLF) for the Province of Taroudant, Morocco, using historical monthly load and weather data for five years (2018 to 2022). To forecast consumed energy three methods are used namely artificial neural network (ANN), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS). This paper selects absolute percentage error (APE), mean absolute percentage error (MAPE), correlation coefficient (R) and root mean square error (RMSE) to compare and evaluate the prediction accuracy of models. It has been observed through results analysis that the ANFIS model produces very accurate forecasting prediction with MAPE of 4.75% while ANN and FL models give respectively MAPE of 7.36% and 8.42%.
Electrocardiogram features detection using stationary wavelet transform Aqil, Mounaim; Jbari, Atman
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp374-385

Abstract

The main objective of this paper is to provide a novel stationary wavelet transform (SWT) based method for electrocardiogram (ECG) feature detection. The proposed technique uses the detail coefficients of the ECG signal decomposition by SWT and the selection of the appropriate coefficient to detect a specific wave of the signal. Indeed, the temporal and frequency analysis of these coefficients allowed us to choose detail coefficient of level 2 (Cd2) to detect the R peaks. In contrast, the coefficient of level 3 (Cd3) is determined to extract the Q, S, P, and T waves from the ECG. The proposed method was tested on recordings from the apnea and Massachusetts Institute of Technology–Beth Israel hospital (MIT-BIH) databases. The performances obtained are excellent. Indeed, the technique presents a sensitivity of 99.83%, a predictivity of 99.72%, and an error rate of 0.44%. A further important advantage of the method is its ability to detect different waves even in the presence of baseline wander (BLW) of the ECG signal. This property makes it possible to bypass the filtering operation of BLW.
Co-Authors Atillah, Mohamed amine Boudaoud, Abdelghani Jbari, Atman Stitou, Hicham