Improved Neural Networks with Random Weights for Short-Term Load Forecasting

Kun Lang, Mingyuan Zhang and Yongbo Yuan

PLOS ONE, 2015, vol. 10, issue 12, 1-14

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0143175

DOI: 10.1371/journal.pone.0143175

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