This paper proposes a short-term electrical load forecasting method based on a BiGRU-Attention network optimized by an improved multi-strategy black-winged kite algorithm (IMBKA), which achieves favorable forecasting performance among the compared models.
Abstract
Accurate short-term electrical load forecasting is of paramount importance for economic dispatch, reliable grid operation, and efficient demand-side management. However, hybrid forecasting frameworks constructed with deep learning models exhibit strong sensitivity to hyperparameter settings. Moreover, swarm-intelligence optimization algorithms are prone to premature convergence when tuning the hyperparameters of forecasting models, thereby degrading prediction performance. In addition, complex load sequences contain local fluctuations and key temporal segments that are difficult to capture using a single recurrent architecture. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a BiGRU-Attention network optimized by an improved multi-strategy black-winged kite algorithm (IMBKA). The BiGRU extracts bidirectional temporal dependencies from historical load windows, while the attention module assigns adaptive weights to informative time steps and suppresses redundant historical information. To improve hyperparameter optimization, IMBKA introduces Sobol sequence initialization and adaptive elite differential mutation. Sobol sequence initialization enhances population coverage, and adaptive elite differential mutation strengthens information exchange among high-quality individuals. Experimental results on electrical load datasets from Singapore, Australia, and Belgium show that IMBKA-BiGRU-Attention achieves favorable forecasting performance among the compared models. The proposed model obtains RMSE values of 70.07 MW, 159.49 MW, 231.82 MW, and 163.43 MW in the Singapore, Australian, Belgian weekday, and Belgian weekend experiments, respectively. Compared with the best-performing model among the evaluated baselines in each experiment, the RMSE is reduced by 4.65%, 16.48%, 3.34%, and 11.39%, respectively.
Industrial power load exhibits severe fluctuations with multi-scale seasonality coexisting with discrete operating states, making it difficult for traditional single models to accurately capture its complex dynamics. To address these issues, this paper proposes a short-term power load forecasting method based on eXtreme Gradient Boosting (XGBoost) and multi-dimensional feature engineering. By constructing a five-dimensional feature engineering framework that integrates temporal features, multi-scale lagged variables, rolling statistics, and electrical domain parameters, the time series forecasting problem is transformed into a structured supervised learning problem. Time-series-aware cross-validation and grid search strategies are employed to achieve systematic hyperparameter optimization. Validated on full-year 2018 operational data at 15-minute resolution from a steel enterprise, the optimized model achieves a Mean Absolute Percentage Error (MAPE) of 7.3% on the test set, representing a 48.6% improvement over the naive persistence baseline. Ablation experiments and feature importance analysis reveal the independent contributions of each feature category and the core driving factors of load variation, providing a practical solution that balances accuracy, efficiency, and interpretability for short-term load forecasting in industrial scenarios.
Yulong Feng, Bin Du, Guo-Long Yang et al.· 2026 5th International Confe...· 0 citations
An integrated algorithm based on improved Bidirectional Long Short-Term Memory and Deep Reinforcement Learning provides an effective solution for intelligent load prediction and adaptive energy management, offering practical support for electromagnetic energy distribution and resilient operation in next-generation smart power systems.
Y.-F. Zeng, Y.-Y. Wang, M.-K. Li et al.· Advanced Electromagnetics· 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
In today's power industry, the ever-changing load environment makes accurate load forecasts even more important than ever. Even, day ahead load forecasting is crucial for the successful and efficient operation of the grid. With the absence of good reliable forecasts of daily load fluctuations, systems within the grid can become overloaded, resulting in a failure of the system, thus establishing the need for sound forecasting methods. As a solution to this challenge, different types of deep learning algorithms namely, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network based LSTM, Convolutional Neural Network based BiLSTM are applied for short-term load forecasting in this paper. The efficacy of the various deep learning techniques for short term load prediction is compared. The technique which produces the least value of root mean squared error between real and estimated load, is the most preferred one. The comparative analysis shows that Convolutional Neural Network based BiLSTM technique is a better option for short term load forecasting.
Jaya Christa Sargunar Thomas, Krishna Veni Kaliappan Sivakumar· ITEGAM- Journal of Engineeri...· 0 citations
These findings challenge the prevailing view of deep learning dominance in time-series forecasting and establish new accuracy benchmarks for electricity load prediction.
Ahmed Rashed Almesmari, M. Al-Betar, S. Makhadmeh· Journal of Advances in Infor...· 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
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