Electricity Demand Forecasting With a Modified Extreme-Learning Machine Algorithm - Chen, Chen; Ou, Chuangang; Liu, Mingxiang; Zhao, Jingtao (Frontiers in Energy Research, 2022)
In order to operate the power grid safely and reduce the cost of power production, power load forecasting has become an urgent problem to be solved. Although many power load forecasting models have been proposed, most of them still suffer from poor model training, limitations sensitive to outliers, and overfitting of load forecasts. The limitations of current load forecasting methods may lead to the generation of additional operating costs for the power system, and even damage the distribution and network security of the related systems. To address this type of problem, we propose a new load prediction model with mixed loss functions. The model is based on Pinball-Huber's extreme learning machine and whale optimization algorithm. Specifically, the Pinball-Huber loss, which is insensitive to outliers and largely prevents overfitting, is proposed as the objective function for ELM training. On the basis of Pinball-Huber ELM, we added the whale optimization algorithm to improve it. Finally, we verify the effect of the proposed hybrid loss function prediction model with two real power load datasets (Nanjing and Taixing). Experimental results have confirmed that the proposed hybrid loss function load prediction model can achieve satisfactory improvements on both datasets.