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Power loads usually have multiple cycles, and the traditional forecasting methods only use the historical loads under various types of cycles together as input features to construct the forecasting model, ignoring the deep features under multiple cycles. The simultaneous inputs will lead to the cycles overlapping and influencing each other, which will be difficult to deal with when building the model. Therefore, this paper proposes a GRO-Bagging day-ahead power curve forecasting model based on multi-cycle feature extraction. First, the multi-periodicity of the power load is analyzed. The one-dimensional time series is converted to two-dimensional according to the multiple cycles of power load. Then, the multi-periodic feature extraction is performed by a multi-size convolutional feature extraction layer with parallel selected based on the data characteristics and modeling mechanism, and Bootstrap Aggregating (Bagging) method is used to construct different prediction models for the datasets containing different periodic features; finally, Gold Rush Optimization (GRO) algorithm is introduced and improved by using the Tent chaotic mapping and elite strategy, and the improved optimization algorithm is used for the weight optimization of the model, the error feedback mechanism is introduced to achieve the weight dynamic Updating. To prove the superiority of the proposed model, a series of comparison experiments and ablation experiments are carried out on real datasets, and the results show that the proposed method has higher prediction accuracy, and prove that the multi-period feature extraction and dynamic weighting methods have a positive and active effect on load prediction.

The optimal design of real-scale structures under frequency constraints is a crucial problem for engineers. In this paper, linear analysis, as well as optimization by considering natural frequency constraints, have been used for real-scale symmetric structures. These structures require a lot of time to minimize weight and displacement. The cyclically symmetric properties have been used for decreasing time. The structure has been decomposed into smaller repeated portions termed substructures. Only the substructure elements are needed when analyzing and designing with the concept of cyclic symmetries. The frequency constrained design of real-scale structures is a complex optimization problem that has many local optimal answers. In this research, the Gold Rush Optimization (GRO) algorithm has been used to optimize weight and displacement performances due to its effectiveness and robustness against uncertainties. The efficacy of the concept of cyclic symmetry to minimize the time calculated is assessed by three examples, including Disk, Silo, and Cooling Tower. Numerical results indicate that the proposed method can effectively reduce time consumption, and that the GRO algorithm results in a 14–20% weight reduction of the problems. [ABSTRACT FROM AUTHOR]

The optimal design of real-scale structures under frequency constraints is a crucial problem for engineers. In this paper, linear analysis, as well as optimization by considering natural frequency constraints, have been used for real-scale symmetric structures. These structures require a lot of time to minimize weight and displacement. The cyclically symmetric properties have been used for decreasing time. The structure has been decomposed into smaller repeated portions termed substructures. Only the substructure elements are needed when analyzing and designing with the concept of cyclic symmetries. The frequency constrained design of real-scale structures is a complex optimization problem that has many local optimal answers. In this research, the Gold Rush Optimization (GRO) algorithm has been used to optimize weight and displacement performances due to its effectiveness and robustness against uncertainties. The efficacy of the concept of cyclic symmetry to minimize the time calculated is assessed by three examples, including Disk, Silo, and Cooling Tower. Numerical results indicate that the proposed method can effectively reduce time consumption, and that the GRO algorithm results in a 14–20% weight reduction of the problems.