Predicting Spare Parts Inventory of Hydropower Stations and Substations Based on Combined Model
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 autocorrel...
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| Veröffentlicht in: | Mathematical problems in engineering 2022, Vol.2022, p.1-11 |
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| container_title | Mathematical problems in engineering |
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| creator | Ma, Zhenguo Tang, Bing Zhang, Keqi Huang, Yuming Cao, Danyi Luo, Jiaohong Zhang, Jianyong |
| description | 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. |
| doi_str_mv | 10.1155/2022/1643807 |
| format | Article |
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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.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/1643807</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Accuracy ; Artificial intelligence ; Autoregressive moving average ; Back propagation networks ; Deep learning ; Error analysis ; Forecasting ; Fourier series ; Hydroelectric power ; Hydroelectric power stations ; Inventory ; Inventory control ; Inventory management ; Machine learning ; Methods ; Neural networks ; Power supply ; Spare parts ; Substations ; Time series</subject><ispartof>Mathematical problems in engineering, 2022, Vol.2022, p.1-11</ispartof><rights>Copyright © 2022 Zhenguo Ma et al.</rights><rights>Copyright © 2022 Zhenguo Ma et al. 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| subjects | Accuracy Artificial intelligence Autoregressive moving average Back propagation networks Deep learning Error analysis Forecasting Fourier series Hydroelectric power Hydroelectric power stations Inventory Inventory control Inventory management Machine learning Methods Neural networks Power supply Spare parts Substations Time series |
| title | Predicting Spare Parts Inventory of Hydropower Stations and Substations Based on Combined Model |
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