Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-8· 0 citations· 31 references
Abstract
Accurate short-term load forecasting (STLF) is essential for modern grid operations, enabling efficient scheduling, demand response, and renewable energy integration. This paper presents a systematic comparison of five forecasting architectures applied to a large dataset of 98 residential homes, with 1-minute and 15-minute smart meter readings spanning 2015-2023. The models include an XGBoost pipeline with extensive feature engineering, a tuned CatBoost implementation, a feedforward neural network with multi-output regression, a multi-scale convolutional Kolmogorov-Arnold network (MCKAN), and a long short-term memory (LSTM) network with a 7-day lookback. All models are trained globally, pooling data across homes while incorporating home-specific categorical variables, including an assignment to a simulated microgrid topology with nine kiosks and three phases. Hyperparameter optimization is performed using Optuna and Keras Tuner. CatBoost achieves the lowest test MAE across all horizons, from 0.4089 (15-minute) to 0.7419 (30-day), outperforming XGBoost by 4-10% and deep learning models by larger margins. The findings support Sustainable Development Goals 7, 9, and 13 and provide actionable insights for energy management, particularly in the South African context of load shedding and grid decarbonization.
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 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
Accurate short-term net-load forecasting is critical for the reliable operation of power systems integrating renewable energy sources such as photovoltaic (PV) systems. The inherent variability of PV generation and human-driven demand patterns complicates energy scheduling and storage management. To address these challenges, this study proposes a hybrid residual-transformer ensemble framework that integrates adaptive blending and behavioral baseline learning to improve the accuracy and robustness of forecasts. For gross load forecasting, the framework separates training into nighttime and daytime phases: a single nighttime model is trained on all days, while daytime models are weekday-specific and operate on residuals from monthly–weekday baselines representing habitual load behavior. Instead of directly predicting total load, the model predicts deviations from a holiday-aware baseline that can incorporate recent anomalies computed over the preceding week. These residuals are learned using PatchTST transformer networks and merged with the baseline through an adaptive weighting rule that increases the weight on the baseline during anomalous periods. PV generation is modeled independently using dual Sequence-to-Sequence (Seq2Seq) Transformer networks trained under sunny and non-sunny regimes, incorporating meteorological inputs comprising solar irradiance and temperature, regime blending, and daylight masking to maintain physical consistency. The final net-load forecast is obtained by subtracting the PV predictions from the blended gross load estimates. Evaluations using one year of 15-minute data from three real-world sites spanning distinct climates (a University of Hawaii building, an Australian residential prosumer, and a Madeira Island prosumer) demonstrate improved accuracy and robustness relative to the baseline, supporting the practical feasibility of the framework for renewable-rich distribution systems.
Numan Uddin, Saeed Sepasi, R. Ghorbani· IEEE Access· 0 citations
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
The experimental findings indicate that Linear Regression (LR) model is better than the Artificial Neural Network (ANN) model because it has a small Root Mean Square Error (RMSE), which means that the underlying data set is more linear in nature and in this case, simpler models can be more effective than the more complex nonlinear models.
Shorya Mittal, N. Saxena, K. Gandhi et al.· Journal of Electrical System...· 0 citations
A systematic comparison of Long Short-Term Memory and transformer-based architectures for deterministic short-term PV power forecasting using publicly accessible data from multiple climatic regions highlights the advantages of attention-based sequence modeling for PV applications and offers practical guidance on feature design, input horizon selection, and hyperparameter ranges for future data-driven PV forecasting studies.
Marcel Lüdecke, Elias Oppermann, Michel Meinert et al.· e+i Elektrotechnik und Infor...· 0 citations
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