Aug 2026· Science Journal of Energy Engineering· 0 citations· 18 references
TL;DR
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.
Abstract
TAccurate short-term electricity consumption forecasting is essential for operational planning, reserve allocation, and energy management in modern power systems. This study investigates the performance of recurrent and hybrid attention-based deep learning architectures for short-term electricity consumption forecasting, a sase study in Togo. The proposed framework integrates electricity consumption, meteorological, demographic, and temporal information in order to capture both intrinsic temporal dependencies and exogenous influences affecting electricity demand. Four forecasting architectures were evaluated using a weekly temporal window of 168 hours: LSTM-only, LSTM-decoder, LSTM-attention, and a hybrid LSTM--Multi-Head Attention--LSTM model. The experiments included multi-seed evaluation, ablation study, robustness analysis, and statistical comparison using the Wilcoxon signed-rank test. The results show that all models achieved extremely high forecasting accuracy, with coefficients of determination exceeding 0.9998 and MAPE values below 0.004%. The hybrid architectures slightly improved average forecasting performance, while the standalone LSTM model remained highly competitive. The robustness analysis revealed strong sensitivity to noisy inputs but moderate degradation under missing-data conditions. Statistical analysis indicated that the performance differences between architectures were not statistically significant at the 5% level. 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.
Experimental results show that MSCNN-ResLSTM achieves higher forecasting accuracy and greater stability across the full 24-step prediction horizon, consistently outperforming all competing baselines while effectively suppressing recursive error propagation.
Yu-Hang Zhang, Yi-Ting Zhao, Yu-Jing Meng et al.· De Computis· 0 citations
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
This study proposes a hybrid deep learning model based on the Stationary Wavelet Transform (SWT), a Frequency Attention mechanism, and a Temporal Convolutional Network architecture (TCN) for short-term electricity consumption forecasting at the university campus scale. The study utilizes a total of 40,896 observations collected from the central campus of Afyon Kocatepe University between 1 March 2024 and 30 April 2025, at 15 min intervals. Feature ablation analysis was conducted to determine the contribution of the candidate input variables. The results show that the inclusion of meteorological variables did not provide any additional improvement in forecasting accuracy. Therefore, the final compact model uses only historical electricity consumption together with the Hour and Day of Week calendar variables, which are known at the forecast origin. In the proposed model, the historical electricity consumption sequence is first decomposed into different frequency components using SWT, and the more informative frequency scales are adaptively weighted through the Frequency Attention mechanism. The resulting multiscale representation is then processed by the TCN architecture to model temporal dependencies and directly predict electricity consumption one hour ahead. Model performance was evaluated using the MAE, MSE, RMSE, MAPE, SMAPE, and R2 metrics under rolling and anchored walk-forward validation strategies. The proposed SWT + Frequency Attention + TCN model achieved the best overall performance among the evaluated models, with an RMSE of 10.614 and an R2 of 0.942 under the rolling walk-forward strategy and an RMSE of 10.286 and an R2 of 0.946 under the anchored walk-forward strategy. The findings demonstrate that the integration of SWT-based multiscale representation, selective frequency weighting, and TCN-based temporal dependency modeling provides an effective and robust framework for short-term campus electricity consumption forecasting.
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
This study develops a PyTorch-based LSTM model for short-term electricity load forecasting, using South Australian electricity demand data from 2024, and shows that the LSTM model can follow the main movement of electricity demand, but the prediction curve is smoother than the actual curve and some short-term fluctuations remain difficult to capture.
Wen-Jun Guo· Applied and Computational 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
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