Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but the nonlinear load increases the complexity of forecasting. In order to solve the problem of modal aliasing in historical data, and fully explore the relationship between time series characteristics in load data, this paper proposes a gated cyclic network model (SSA–GRU) based on sparrow algorithm optimization. Firstly, the complementary sets and empirical mode decomposition (EMD) are used to decompose the original data to obtain the characteristic components. The SSA–GRU combined model is used to predict the characteristic components, and finally obtain the prediction results, and complete the short-term load forecasting. Taking the real data of a company as an example, this paper compares the combined model CEEMD–SSA–GRU with EMD–SSA–GRU, SSA–GRU, and GRU models. Experimental results show that this model has better prediction effect than other models.
Multilegged robots can adapt to complex terrains, an ability that is highly important for their research and development. To improve the adaptability and fault tolerance of such robots, the modular design concept is applied by an increase in the number of modules. A modular multilegged robot contains a trunk with six modular leg structures that can be removed at will. The interface design of the trunk and legs can achieve good tightness and high strength, thereby ensuring quick disassembly and that the trunk and legs will not fall off while the robot walks. On this basis, the gait of a robot with different numbers of modular legs is designed. Then, kinematic and dynamic models of the robots with different gaits are established, and the motion performance, which provides reference for motion control and motor selection, is analyzed. Experiments show that the robot with different numbers of legs has good motion performance. This study serves as a useful reference for the design of modular multilegged robots.
The purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting. The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. The original signal is decomposed into multiple characteristic components by set empirical mode decomposition. Each feature component is input into the bionic optimized combination model for prediction. Finally, get the load forecasting results. Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy.
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