Predicting electricity power consumption is an important task which provides intelligence to utilities and helps them to improve their systems' performance in terms of productivity and effectiveness. Machine learning models are the most accurate models used in prediction. The goal of our study is to ...
To demonstrate the effectiveness of the proposed approach on the system behaviors, a simulation test was carried out in the Matlab/Simulink environment, using a real wind profile of a Moroccan city (Essaouira). In comparison to the classical DPC control, the simulation results showed the superior...
This study presents a robust approach for anomaly detection in power consumption time series data collected from the SCADA system of Amendis in Tetouan City, Morocco. Investigating both anomaly detection for unlabeled data and the evaluation of unsupervised anomaly detection in power consumption, the ...
This model added the seasonality of data and other factors on the basis of the Transfomer, carried out LTLF for the power consumption of 321 customers, and achieved good performance. Due to the complexity of power load data, the researchers found that the accuracy of a single model is ...
In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 ...