Energies

Accurate cooling load forecasting supports energy-efficient operation in large public buildings such as airports. Cooling load time series are often nonlinear and temporally dependent, with frequent operating condition changes and pronounced thermal inertia, which limits the reliability of single-model forecasting. This study proposes a cluster- and temperature-aware auto-ensemble model (CATS-Ens) for short- and long-term cooling load prediction. CATS-Ens learns condition-dependent model contributions within temperature-based operating intervals and distinct load regimes, enabling collaborative prediction across complementary experts and avoiding reliance on a single globally optimal predictor. The proposed model is evaluated on a real-world hourly cooling load dataset collected from an airport terminal. Results show that CATS-Ens achieves consistently better performance than representative baselines under multiple metrics, including MAE, RMSE, MAPE, sMAPE, and $R^2$. Compared with the best individual baseline, CATS-Ens reduces MAE by 8.5%, RMSE by 8.4%, MAPE by 12.6%, and sMAPE by 7.1%, with an $R^2$ of 0.967. The model maintains stable accuracy under varying operating conditions and alleviates false-positive predictions during zero-load and low-load periods, demonstrating its practical value for cooling load forecasting in complex building energy systems.

The reliable operation of transmission lines is essential for grid stability. Growing electricity demand pushes existing lines to full capacity, while new construction is constrained by resources and the environment. Dynamic capacity increase technology addresses this by boosting transmission capacity without physical upgrades, with the identification of weak points along the line being central to its application. This study integrates correlation analysis and the Analytic Hierarchy Process to develop an evaluation method for transmission line segments, with a supporting software implementation also developed. A system of characteristic quantities was first established using operation and maintenance guidelines combined with correlation analysis. The Analytic Hierarchy Process was applied to score features and derive weights after consistency validation. Preprocessed line data were then weighted to calculate segment weakness levels, and fuzzy comprehensive evaluation was used for both qualitative and quantitative condition analysis. The model was validated through a case study, and its software implementation streamlines and enhances the assessment process.

Driven by the large-scale application of distributed power sources, power systems are facing escalating frequency stability challenges in terms of inertia reduction. In this weak grid scenario, grid-connected converters are increasingly required to operate as high-inertia grid-forming (GFM) units to participate in the regulation of grid frequency. However, this high inertia will seriously impair the transient stability of GFM converters. To resolve the conflict, an adaptive hybrid synchronization-based transient enhancement strategy is proposed. Through integrating the traditional droop phase angle with the phase-locked loop-locked grid phase angle, the proposed control can effectively enhance transient stability under the full fault range from mild to severe voltage sags (with a voltage sag depth of up to 90%) without sacrificing system inertia. Moreover, benefiting from this, the proposed hybrid synchronization scheme also avoids the secondary overcurrent issue that occurs after fault clearance in traditional GFM control. Finally, the simulation and experimental results under various voltage sags verify the effectiveness of the proposed control strategy.