The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, thus leading to increased economic and social benefits. Currently, some simple AI and hybrid models have issues to deal with and struggle with multivariate dependencies, long-term dependencies, and nonlinear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while lasso regression serves as the metalearner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in the forecasting accuracy, with a substantial reduction in the mean absolute percentage error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, thus suggesting potential operational efficiency benefits for the power industry.

This paper investigates the event-triggered observer-based output feedback load frequency control (LFC) problem for power systems. To reduce the amount of transmitted signals, a dynamic event-triggered scheme is proposed by adding an exponential term. Moreover, an adaptive event-triggered scheme is proposed to provide a balance between the control performance and the number of transmitted signals. Under the proposed schemes, a new model is formulated for the observer-based output feedback LFC system via a time-delay system method. By employing Lyapunov functional method, sufficient conditions are derived for global asymptotical stability and $\mathcal L_{\infty}$ performance. Then, a controller design method is developed. Finally, two examples are given to illustrate the effectiveness of the proposed schemes.