Spare Parts Demand Forecasting in Energy Industry: A Stacked Generalization-Based Approach

Yu-Chung Tsao, N. Kurniati, I. N. Pujawan, Alvin Muhammad 'Ainul Yaqin
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Abstract

This paper deals with spare parts demand forecasting problem in energy industry. Forecasting spare parts demand has its own challenges because in general spare parts demand is characterized by high variation in its demand size and in its inter-demand interval. In this paper, a forecasting approach to deal with spare parts demand is proposed. The proposed approach utilized stacked generalization technique to combine traditional time series forecasting method and machine learning method into a single ensemble. To test its performance, a case study in a natural gas liquefaction company is provided in this paper. In the case study, the proposed approach is utilized to forecast the monthly demand of spare parts used for maintenance operations. To compare its performance, several traditional time series forecasting methods (including Moving Average, Single Exponential Smoothing, Croston's method, Syntetos-Boylan Approximation, and Teunter-Syntetos-Babai) and several machine learning methods (including Linear Regression, Elastic Net, Neural Network, Support Vector Machine, and Random Forests) are also used in the case study. As results, the proposed approach performed better than other methods in terms of forecast error minimization.
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能源行业备件需求预测:一种基于堆叠的通用方法
本文研究了能源行业备件需求预测问题。备件需求预测本身就具有挑战性,因为一般来说,备件需求的特点是其需求规模和需求间隔的高度变化。本文提出了一种备件需求预测方法。该方法利用叠加泛化技术将传统的时间序列预测方法和机器学习方法结合为一个集成。为了验证其性能,本文以某天然气液化公司为例进行了研究。在实例研究中,利用所提出的方法预测维修操作所需备件的月需求量。为了比较其性能,在案例研究中还使用了几种传统的时间序列预测方法(包括移动平均、单指数平滑、Croston方法、Syntetos-Boylan近似和Teunter-Syntetos-Babai)和几种机器学习方法(包括线性回归、弹性网络、神经网络、支持向量机和随机森林)。结果表明,该方法在预测误差最小化方面优于其他方法。
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