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Rena Gao, Jingxuan Wu, Xuetong Wu, Carsten Roever, Jing Wu, Long Lv, Jey Han Lau. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Causal feature selection has recently received increasing attention in machine learning and data mining, especially in the era of Big Data. Existing causal feature selection algorithms select unique causal features of the single class label as the optimal feature subset. However, a single class label usually has multiple classes, and it is unreasonable to select the same causal features for different classes of a single class label. To address this problem, we employ the class-specific mutual information to evaluate the causal information carried by each class of the single class label, and theoretically analyze the unique relationship between each class and the causal features. Based on this, aLabel-awareCausalFeatureSelection algorithm (LaCFS) is proposed to identifies the causal features for each class of the class label. Specifically, LaCFS uses the pairwise comparisons of class-specific mutual information and the size of class-specific mutual information values from the perspective of each class, and follows a divide-and-conquer framework to find causal features. The correctness and application condition of LaCFS are theoretically proved, and extensive experiments are conducted to demonstrate the efficiency and superiority of LaCFS compared to the state-of-the-art approaches.

For multibus wind power plants in microgrids, it is challenging to develop a reliable, effective, and robust harmonic suppression method for harmonic voltages and currents of all buses. This paper proposes a hierarchical harmonic control method to mitigate the harmonic voltages and currents of all buses in grid-forming wind power plants. The proposed method effectively reduces the terminal harmonic voltages and currents, PCC harmonic voltage, and grid harmonic current. It achieves adaptive harmonic mitigation and automatic harmonic current sharing between wind turbine units according to the rated capacity and feeder impedance of each unit. The effectiveness of the method is verified in the planned Turkish offshore wind power plant by simulation in Digsilent/PowerFactory.

A fuzzy logic based online energy management system (FLEMS) is designed in this paper to achieve the optimal electricity cost in a residential Microgrid (MG). The proposed FLEMS is combined by a local energy price model (LEPM) and a fuzzy-logic strategy. The LEPM will preprocess the sampling data to estimate the electricity market and local MG status. The fuzzy-logic mimics the artificial intelligent assessment to economic issues and make decision for the charging and discharging operation for energy storage system (ESS). In the FLEMS, not only electricity price and supply-demand balance, but also ESS state of charge are considered for the efficient and stable operations. The proposed method does not relay on the accurate prediction of renewable energy source and local loads. Historical experience of the system is involved by the LEPM and guides the ESS operation in the fuzzy-logic. A real-world data based household-level residential MG model is established to validate the performance of the FLEMS. A hourly-resolution-Particle swarm optimization (PSO) with perfect day-ahead prediction is implemented as the baseline to verify the superiority of the proposed method.