Application of Mathematical Models in Forecasting the Usage of Water, Electricity and Gas in Hospital Logistics

Abstract

To explore the prediction effect of mathematical models on water, electricity and gas usage in hospital logistics operations, in order to obtain the best prediction model and improve the level of hospital logistics refinement management. Monthly data of water, electricity and gas usage of hospital logistics from January 2016 to December 2020 were selected for the training and testing of the model, a nonlinear self-organizing regression neural network model and an exponential smoothing correlation model were constructed, and the prediction effect was evaluated and analyzed by evaluation indexes. The goodness-of-fit of the NARNN model reached 0.87047 on the training data set of water usage, 0.91166 on the validation set, and 0.94479 on the test set, with an overall model goodness-of-fit value of 0.88075, and an overall goodness-of-fit value of 0.95808, close to 1, in the prediction of gas usage, and the seasonal multiplication model in the water and electricity The RMSEs of the seasonal multiplication model in both water and electricity usage forecasting reached the lowest. All the mathematical models used in this paper can effectively predict the water, electricity and gas usage data. By evaluating the index results, a comprehensive comparison shows that the nonlinear self-organizing neural network model has the best prediction effect and can provide prediction data support for information decision making and energy saving and emission reduction deployment in the hospital logistics department.