基于改进多元宇宙算法优化ELM的短期电力负荷预测

为改善因人工神经网络参数随机初始化对短期电力负荷预测带来的不足，提出一种基于改进多元宇宙(improved multivariate universe optimizer, IMVO)算法优化极限学习机(extreme learning machine, ELM)的短期电力负荷预测方法。算法的改进包含3个方面。首先，添加beta分布的随机数得到改进Tent混沌映射方法，采用遍历均匀性更好的改进Tent混沌映射方法使MVO算法得到好的初始解位置。其次，采用指数形式改进传统MVO算法的旅行距离率，利用指数形式改进后可使算法在整个寻优迭代前中期保持较高的全局开发水平。然后，采用精英反向学习的方法改进宇宙群。通过基准函数测试改进前后算法的性能，表明IMVO算法具有更好的稳定性和鲁棒性。最后，利用IMVO算法优化ELM的权值和阈值，建立IMVO-ELM短期电力负荷预测模型。通过实例分析和实验对比，表明IMVO-ELM模型的稳定性、预测精度和泛化能力均优于其他模型。

Short-term power load forecasting based on an improved multi-verse optimizer algorithm optimized extreme learning machine

To help overcome the shortcomings of short-term power load forecasting caused by random initialization of artificial neural network parameters, a forecasting method based on an improved multivariate universe optimizer (IMVO) algorithm and an extreme learning machine (ELM) is proposed. The improvement of the algorithm includes the following three aspects. First, the improved Tent chaotic mapping method is obtained by adding the random number of beta distribution, and the improved Tent chaotic mapping method with better ergodic uniformity is used to make the MVO algorithm obtain a good initial solution. Second, the travel distance rate of the traditional MVO algorithm is improved using the exponential form, and the improved algorithm can maintain a high global development level in the whole optimization iteration before and during the middle period. Then, the elite reverse learning method is used to improve the universe group. The performance of the algorithm before and after improvement is tested by the benchmark function, indicating that the IMVO algorithm has better stability and robustness. Finally, the IMVO algorithm is used to optimize the weights and thresholds of an ELM, and the IMVO-ELM short-term power load forecasting model is established. Case analysis and comparative experiments show that the stability, prediction accuracy and generalization ability of IMVO-ELM model are better than those of other models. This work is supported by the National Natural Science Foundation of China (No. 52177085).