title: Improved Neural Networks with Random Weights for Short-Term Load Forecasting.
abstract: An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.
authors.affiliation: Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Lang
authors.affiliation: Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China.
authors.forename: Mingyuan
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China.
authors.forename: Yongbo
authors.initials: Y
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D011211:Electric Power Supplies; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics