Predicting Spare Parts Inventory of Hydropower Stations and Substations Based on Combined Model

Abstract

In this paper, a combined model is proposed to predict spare parts inventory in accordance with equipment characteristics and defect elimination records. Fourier series is employed to process the periodicity of the data, autoregressive moving average (ARMA) is used to deal with the linear autocorrelation of the data, and backpropagation (BP) neural network is used to settle the nonlinearity of the data. The prediction results, comparisons, and error analyses show that the combined model is accurate and meets the practical requirements. The combined model not only fully utilizes the information contained in the data but also provides a reasonable decision basis for the procurement of spare parts, making the inventory in a safe state and saving holding costs.