Intelligent Neural Network with Parallel Salp Swarm Algorithm for Power Load Prediction

作     者：Zhou, Jin-Liang Chu, Shu-Chuan Tian, Ai-Qing Peng, Yan-Jun Pan, Jeng-Shyang 

作者机构：Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Shandong Peoples R China Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Chaoyang Univ Technol Dept Informat Management Taichung Taiwan 

出 版 物：《JOURNAL OF INTERNET TECHNOLOGY》 (J. Internet Technol.)

年 卷 期：2022年第23卷第4期

页      面：643-657页

核心收录：

主　　题：Power load forecast Salp swarm algorithm Artificial neural network Particle swarm algorithm 

摘      要：The neural network runs slowly and lacks accuracy in power load forecasting, so it is optimized using meta-heuristic algorithm. Salp Swarm Algorithm (SSA) is a novel meta-heuristic algorithm that simulates the salp foraging process. In this paper, a parallel salp swarm algorithm (PSSA) is proposed to improve the performance of SSA. It not only improves local development capabilities, but also accelerates global exploration. Through 23 test functions, PSSA performs better than other algorithms and can effectively explore the whole search space. Finally, PSSA is used to optimize the weights as well as the thresholds of the neural network. Using the optimized neural network to predict the power load in a certain region, the results show that PS SA can better optimize the neural network and increase its prediction accuracy.