Accurate short-term load forecasting is of great significance to the safe and stable operation power systems development market. Most existing studies apply deep learning models make predictions considering only one feature or temporal relationship in time series. Therefore, obtain an accurate reliable prediction result, a hybrid model combining dual-stage attention mechanism (DA), crisscross g...

Traffic-flow prediction plays an important role in the construction of intelligent transportation systems (ITS). So, order to improve accuracy short-term traffic flow prediction, a model (GWO-attention-LSTM) based on combination optimized attention mechanism and long memory (LSTM) is proposed. The LSTM uses assign individual weight feature information extracted via LSTM. This can increase model...

<p><span>The current work aims to use traditional control algorithms and advanced optimization that was chosen for its ease of the possibility using it in many industrial applications. By setting appropriate specifications simulation model after conducting planned tests simulate different applications motor’s within electrical systems, results proved obtain good performance work, be...

Perhaps the two most significant theoretical questions about the programming of selfassembling agents are: (1) necessary and sufficient conditions to produce a unique terminal assembly, and (2) error correction. We address both questions, by reducing two well-studied models of tile assembly to models of distributed shared memory (DSM), in order to obtain results from the memory consistency syst...

Abstract In the field of transformer fault diagnosis, there are many diagnostic methods. These methods either not high accuracy or used too long. this paper, a dimension learning-based hunting (DLH) search strategy optimization grey wolf algorithm support vector machine (DLH-GWO-SVM) model was proposed. process initializing population, logistic chaotic mapping to improve quality initial positio...

Abstract In short-term power load forecasting, changes in external multi-dimensional factors will have a certain impact on the accuracy of forecasting. response to this problem, paper proposes prediction model GWO-BILSTM that combines Grey Wolf Optimizer (GWO) and bi-directional long memory (BILSTM) network. Taking real data as set, high correlation parameters are selected input through Pearson...

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving problem distribution network reconfiguration (DNR) optimal location sizing distributed generations (DGs) simultaneously in radial systems (RDSs). is being used significantly to minimize system power loss, voltage deviation at load buses improve profile. When high-dimension...

Abstract Strong noise, nonlinearity, and complex frequency are the remarkable symbols of vibration signals when rolling bearing failure. So a new method that improved accuracy fault diagnosis is proposed based on variational mode decomposition (VMD) grey wolf algorithm optimized extreme learning machine (GWO-ELM). The signal decomposed by VMD, Intrinsic Mode Component (IMF) used for fuzzy entro...