A multi-level short-term load forecasting (STLF) model based on an improved federated learning (FL) framework aimed at serving both distribution systems and power users is developed, demonstrating its ability to ensure rapid training, high accuracy, and multi-level prediction while protecting user data privacy.
Short-term power load forecasting is crucial for power system dispatch and market transactions. However, the dynamic, nonlinear, and uncertain nature of loads poses challenges for accurate interval estimation. This paper proposes a short-term load interval estimation algorithm based on online sequence extreme learning...
Ya-Xing Wei, Jia-Mao Han, Bin Zhao et al.· Journal of Measurements in E...· 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 compl...
Shorya Mittal, N. Saxena, K. Gandhi et al.· Journal of Electrical System...· 0 citations
Short-term power load forecasting plays a crucial role in the operation scheduling, energy management, and security assessment of smart grids, as its accuracy directly affects the economic efficiency and reliability of power systems. However, power load series are characterized by strong nonlinearity, non-stationarity,...
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 smar...
Y.-F. Zeng, Y.-Y. Wang, M.-K. Li et al.· Advanced Electromagnetics· 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 dema...
Timur Lale· 2026 6th International Confe...· 0 citations
Accurate load forecasting (LF) and effective anomaly detection (AD) at the individual household level are crucial for ensuring efficient energy management in smart grids. Yet, it remains challenging due to high variability in user behavior and data anomalies. This paper proposes an integrated two-stage consumer level f...
T. Gupta, Richa Bhatia, Richa Sharma· IEEE Access· 0 citations
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