@article{4797,
  author = {Hajar Ait Lamkademe},
  title = {High-Resolution Smart Meter Load Forecasting in Morocco: Model Performance, Diagnostics, and Interpretability Analysis for Laâyoune Zone 1},
  journal = {Journal of Electronic Systems},
  year = {2026},
  volume = {16},
  number = {3},
  doi = {https://doi.org/10.6025/jes/2026/16/3/163-178},
  url = {https://www.dline.info/jes/fulltext/v16n3/jesv16n3_3.pdf},
  abstract = {Accurate short-term load forecasting is essential for the efficient and reliable operation of modern smart
grids. This study develops and comprehensively evaluates a high-resolution, interpretable load forecasting
framework using 10-minute smart meter data from Laâyoune Zone 1, Morocco. To capture complex temporal
dependencies, we engineered advanced features, including lag variables, rolling statistics, and cyclical time
encodings. Multiple machine learning models were systematically compared, with the Random Forest (RF)
ensemble achieving the best overall performance (RMSE: 5.42, R²: 0.87), effectively capturing nonlinear
demand dynamics. Notably, the unexpectedly strong performance of the Persistence baseline highlighted
the significant short-term autocorrelation inherent in high-frequency residential and industrial load data.
Multi-step forecast horizon analysis revealed exceptional accuracy for 10 to 60 minute predictions, making
the models highly suitable for real-time grid balancing and transformer management, although uncertainty
gradually accumulates beyond two hours. Residual diagnostics further confirmed minimal systematic bias
and stable model behavior across varying demand conditions. Furthermore, SHAP-based interpretability
analysis confirmed that recent lagged load measurements and temporal indicators are the primary drivers
of predictions, overcoming the traditional “black-box” limitations of machine learning. Ultimately, this
explainable ensemble framework provides a robust, transparent, and computationally efficient tool for
utility operators, facilitating demand response, anomaly detection, and sustainable energy management in
modern smart grid environments.},
}