Residential-level short-term load forecasting (STLF) is significant for power system operation. Data-driven forecasting models, especially machine-learning-based models, are sensitive to the amount of data. However, privacy and security concerns raised by supervision departments and users limit the data for sharing. Meanwhile, the limited data from the newly built houses are not sufficient to support building a powerful model. Another problem is that the data from different houses are in a non-identical and independent distribution (non-IID), which makes the general model fail in predicting accurate load for the specific house. Even though we can build a model corresponding to each house, it costs a large computation time. We first propose a federated transfer learning approach applied in STLF, deep federated adaptation (DFA), to deal with the aforementioned problems. This approach adopts the federated learning architecture to train a global model without undermining privacy, and then the model leverage multiple kernel variant of maximum mean discrepancies (MK-MMD) to fine-tune the global model, which makes the model adapted to the specific house’s prediction task. Experimental results on the real residential datasets show that DFA has the best forecasting performance compared with other baseline models and the federated architecture of DFA has a remarkable superiority in computation time. The framework of DFA is extended with alternative transfer learning methods and all of them achieve good performances on STLF.

Magnetic levitation positioning systems have attracted an increasing amount of attention for many modern industrial applications because they can work in a large range of motion and in multiple degrees of freedom (DoFs) with resolutions as small as nanometer and microradian levels. In this paper, a 6-DoF positioning system based on 4 linear magnetic levitation actuators (MLAs) is designed and tested. In contrast to the existing modeling method, this work takes the yaw angle into account in the magnetic force and torque solutions of the linear MLA, and the revised model is employed in motion control. The raw data from the sensing system are processed by the Newton-Raphson method for the position and rotation motions of the translator. The PID compensator controls the equivalent second-order system as the motions in different axes are decoupled by the proposed wrench model. Experimental results related to the motion resolution, motion range, step response, trajectory tracking, and payload capacity are given to evaluate the performance of the prototype. The results show that the stroke of the maglev system is a volume of 20mm×20mm×4mm with a rotational range of 0.05rad×0.05rad×0.2rad and that the positioning precision depends on the resolution of the sensing system.

This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

The Aquila Optimizer (AO) algorithm is a meta-heuristic algorithm with excellent performance, although it may be insufficient or tend to fall into local optima as as the complexity of real-world optimization problems increases. To overcome the shortcomings of AO, we propose an improved Aquila Optimizer algorithm (IAO) which improves the original AO algorithm via three strategies. First, in order to improve the optimization process, we introduce a search control factor (SCF) in which the absolute value decreasing as the iteration progresses, improving the hunting strategies of AO. Second, the random opposition-based learning (ROBL) strategy is added to enhance the algorithm’s exploitation ability. Finally, the Gaussian mutation (GM) strategy is applied to improve the exploration phase. To evaluate the optimization performance, the IAO was estimated on 23 benchmark and CEC2019 test functions. Finally, four real-world engineering problems were used. From the experimental results in comparison with AO and well-known algorithms, the superiority of our proposed IAO is validated.