2026· Journal of Advances in Information Technology· 0 citations· 34 references
TL;DR
These findings challenge the prevailing view of deep learning dominance in time-series forecasting and establish new accuracy benchmarks for electricity load prediction.
Abstract
—This paper investigates electricity load forecasting using machine learning models enhanced with advanced optimization techniques. Six regression-based models—Gradient Boosting, LightGBM, ExtraTrees, Random Forest, Decision Tree, and Long Short-Term Memory (LSTM)—are evaluated on two real-world datasets from Panama City and Tetouan City, across hourly and 10-minute temporal resolutions. Results demonstrate that tree-based ensemble models, particularly the ExtraTreesRegressor, consistently outperform LSTM-based deep learning approaches. A key contribution is the development of an Enhanced Harris Hawks Optimization (EHHO) algorithm, incorporating adaptive parameter control and type-specific parameter handling. EHHO significantly improves hyperparameter tuning efficiency, enabling the ExtraTreesRegressor to achieve state-of-the-art forecasting accuracy. The EHHO-optimized ExtraTreesRegressor attains a Mean Absolute Percentage Error (MAPE) of 0.30% for Tetouan City and 1.47% for Panama City using 10-minute resolution data. The analysis reveals that higher temporal granularity contributes up to 65% improvement in forecasting performance compared to hourly data. These findings challenge the prevailing view of deep learning dominance in time-series forecasting and establish new accuracy benchmarks for electricity load prediction. The proposed methodology holds strong potential for practical deployment in grid operation, demand response, and renewable energy integration, supporting the development of more efficient and resilient energy systems
This study compares two multi-step forecasting strategies for 24-h horizons: a direct 24→24 strategy and a recursive strategy based on sequential 24→1 predictions, and concludes that the selection of a forecasting strategy depends on the required balance between overall accuracy, temporal stability, and computational cost.
Erik Fernando Mendez-Garces, David Buldain, M. Comech· Energies· 0 citations
For smart grid power system planning and operation, short-term load forecasting is crucial. Important decisions including determining system safety, scheduling fuel, economically dispatching electricity, and selling energy can be aided by accurate day-ahead estimates. However, due to its reliance on external variables like weather, the process is intricate and computationally intensive. In order to address this issue, the paper proposes an LSTGR-based architecture that systematically enhances STLF in Smart Grids. The input characteristics are first scaled correctly using data normalisation. A hybrid feature selection method combining XGB and RF is utilised to determine the most essential features. Afterwards, RFE is employed to eliminate superfluous attributes. To facilitate learning, a hybrid deep learning model is trained using the updated dataset. This model combines LSTM and GRU. Using assessment criteria such as MAPE, MAE, MSE, and RMSE, the results demonstrate that the LSTGR model outperforms other models. With an RMSE of only 1.8%, the model clearly excels at producing accurate predictions. All things considered, the model successfully improves the reliability of forecasts while being computationally efficient. Because of this, it is an excellent option for smart grid applications in the actual world.
L. Jayavani, Banoth Ashwini, Kolkur Swabhavika et al.· 2026 7th International Confe...· 0 citations
Overall, the study demonstrates that recurrent deep learning architectures provide reliable and operationally relevant solutions for electricity consumption forecasting in highly structured energy demand series.
Séna Apeke, Yao Bokovi, K. Gbafa et al.· Science Journal of Energy En...· 0 citations
Short-term load forecasting (STLF) is an essential task for reliable power system operation, economic dispatch, reserve scheduling, and grid planning. This study aims to provide an operationally realistic and interpretable comparison of five ensemble tree-based machine learning (ML) models for national electricity demand forecasting using the publicly available Panama Short-Term Electricity Load Forecasting dataset. Gradient Boosting Regressor (GBR), XGBoost, LightGBM, CatBoost, and Random Forest are evaluated using 14 predefined walk-forward train–test splits that emulate the weekly forecasting protocol of Panama’s national grid operator. A common feature set consisting of lagged demand variables, a four-week moving average, temporal indicators, calendar variables, and Tocumen temperature is used for all models. A seasonal naive baseline, statistical significance testing, COVID-period split analysis, and feature importance comparison are also included. CatBoost achieved the best average performance with an RMSE of 55.52 MWh and MAPE of 3.80%, outperforming the seasonal naive baseline, which obtained an RMSE of 78.61 MWh. However, Wilcoxon-Holm testing showed that the narrow RMSE differences among the ensemble models were not statistically significant at the 5% level. Feature importance analysis confirmed that the four-week moving average is a dominant predictor for most models. The results show that ensemble tree-based models provide accurate, robust, and interpretable STLF performance under an operationally realistic evaluation protocol.
Timur Lale· 2026 6th International Confe...· 0 citations
The study shows that machine learning enables smarter load balancing, better integration of renewable energy, and improved decision-making in power distribution systems, and supports the development of intelligent, sustainable, and data-driven energy.
M. Tarani, Tothadi Sowjanya· International Scientific Jou...· 0 citations
Accurate short-term load forecasting is important for running power systems efficiently and managing smart grids. In this study, we present an improved Temporal Convolutional Network (TCN) model and compare eight optimization algorithms: Adam, AdaBelief, RAdam, Ranger, AdamP, NovoGrad, Adan, and SAM. We used hourly electricity load data from Denmark to test each optimizer under the same settings and with three different random seeds to ensure a fair and reliable comparison. All optimizers showed strong predictive accuracy, with root mean square error (RMSE) values between 0.03 and 0.05. Adan and AdamP had the lowest errors and were the most stable. These results show that the choice of optimizer has a big impact on how well TCN models learn and generalize. Our framework offers a solid benchmark for building adaptive and reliable forecasting systems for future smart grids.
Hmeda Musbah, Abdussalam Mohamed, Hamed H. Aly· 2026 IEEE Canadian Atlantic...· 0 citations
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