Short-term load forecasting (STLF) plays a crucial role in the efficient and economical management of power systems. While artificial neural networks have achieved significant success in STLF, they suffer from the limitation of providing a black box representation, making it challenging to obtain an analytical expression between features and loads. This limitation hampers subsequent quantitative analysis, which is crucial for artificial intelligence based decision-making processes. To address this issue, this paper proposes a novel STLF approach through the utilization of Kolmogorov-Arnold Networks (KANs). By leveraging KANs, the interpretability of model parameters can be enhanced. As a result, detailed analytical expressions of the model can be derived. To validate the proposed approach, we conducted experiments by comparing the forecasting performances among KANs, multi-layer perceptrons and XGBoost on a publicly available dataset from Switzerland. Numerical results demonstrate the effectiveness of the proposed KAN-based STLF method in accurately forecasting short-term loads. Additionally, the KAN-based approach provides the advantage of yielding the analytical expression for STLF, enabling further insights and analysis.

Short-term load forecasting (STLF) is important for the operational security and economics of power system. However, most of STLF methods lack an efficient feature selection method to model the time series nonlinearities and feature interaction. In this paper, a new holistic feature selection method is presented. The feedforward long short-term memory network (F-LSTM) is proposed to learn the nonlinear mapping function between features and load. Then, a feature importance matrix is designed to reflect relevancy, redundancy and interaction among the candidate features. Moreover, a hybrid filter-wrapper approach is developed to select suitable features efficiently. The filter part separates useless information for the trained F-LSTM output. The wrapper part selects the optimal subset by fine-tuning the threshold. The results from an empirical study in Switzerland suggest that: 1) the selected subset of features shows high relevancy, low redundancy and high interaction, which is also consistent with the features selected by other feature selection methods, and 2) the proposed method has good prediction performance and can be applied to various artificial neutral network based short-term load forecasting models, which delivers an average 12.1% improvement.

Short-term load forecasting (STLF) plays a crucial role in the efficient and economical management of power systems. The primary challenge of STLF is effectively capturing the long-term dynamic temporal dependency while ensuring accurate multiple steps ahead forecasting. To address this issue, this paper proposes a novel dynamic temporal dependency model (DTDM), which is a modified version of the transformer algorithm for the nature language process to be suitable for implementing the mechanism of STLF. With an encoder-decoder structure, DTDM allows for easy modification of the historical and forecast range, enabling straightforward learning of multi-step temporal dependencies to mitigate error accumulation. Moreover, DTDM leverages the multi-head attention mechanism to recognize and aggregate diverse dynamic temporal dependencies by capturing similarities among different timestamps. Notably, DTDM expands the range of the attention mechanism to encompass both short and long temporal dependencies within the same attention head. We conducted extensive experiments comparing DTDM with several baseline models using a public dataset from Switzerland and a private dataset from Guiyang, China. The two numerical results demonstrate the effectiveness of the proposed model, which achieves a minimum average improvement of 12.22% and 4.28% in terms of mean absolute percentage error, respectively. Furthermore, the attention map visualizations further verify DTDM's ability of modeling dynamic temporal dependency.

Power flow (PF) calculation is essential for power system analysis. In recent years, data-driven methods have emerged as a promising approach to accelerate PF calculations. However, these methods require high-quality labeled data and often suffer from poor generalization. To address these issues, an unsupervised physics-informed neural network (UPINN) method is proposed for AC PF calculations. The proposed method follows the general process of Newton-Raphson’s method. By minimizing the physics-informed loss function, which is designed based on active and reactive power mismatches, the PF equations will be satisfied directly without the need to calculate the Jacobian matrix’s inverse. Proofs of the proposed UPINN training method’s convergence are provided. Case study results on the IEEE 24-bus and 118-bus systems demonstrate the feasibility of the proposed approach, showing that UPINN’s power flow model can achieve high generalization performance without relying on labeled data.

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

Interpretability in short-term load forecasting (STLF) is crucial for power system operations, as it can earn the trust of power system dispatchers, align forecasts with operational practices, and support accountable decisions. Kolmogorov–Arnold Networks (KANs) have gained attention in this setting because of their strong interpretability and symbolic regression capabilities. However, complex black-box artificial neural networks (ANNs) may match or surpass KANs in predictive accuracy, which remains a central concern for dispatchers. To address this trade-off, we introduce a hybrid KAN–ANN model that improves accuracy while retaining interpretability. The model uses residual connections to enforce an additive decomposition of the load, and incorporates an L1 regularization penalty to ensure that the KAN component makes the dominant contribution, thereby maintaining interpretability. We further apply an alternating direction method of multipliers training scheme that switches optimization between the interpretable and residual parts, shrinking the non–interpretable component. In this setup, the KAN captures periodic and nonlinear structure, and the remaining variation is modeled by the ANN. We also visualize the alternating optimization to make the interaction between components clear. We evaluate the method against a state-of-the-art ANN–based STLF model using a public Swiss dataset and a large private dataset from Guiyang, China. Results show that the hybrid KAN–ANN approach improves KAN prediction performance while delivering accurate analytical expressions for STLF. Code is available at: https://github.com/BozhenJIANG/ KAN-ANN-for-STLF.