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In order to solve the problem of difficulty in constructing forecasting models caused by the characteristics of power grid materials, such as many varieties, diverse specifications, huge quantities, wide range of uses, and great influence by policies and investments. Firstly, the factors affecting the quantity of material demand for infrastructure, business expansion, and emergency repair projects were screened by the Delphi method and gray correlation analysis (GRA). Secondly, an improved particle swarm algorithm that introduced adaptive inertia factor and learning factor was utilized to adjust the optimal parameter combinations of the extreme learning machine, and train the material demand prediction models for various distribution network projects. Finally, the results of the GRA-IPSO-ELM (grey relational analysis, improved particle swarm optimization, and extreme learning machines) model were compared with the results of four common forecasting models by taking the demand of 10 kV power cables of a power grid for 2020—2022 infrastructure projects as an example. The results show that the prediction accuracy of the GRA-IPSO-ELM model is improved by 10.38%, 5.37% and 3.83% compared with the ELM model, the support vector machine model and the PSO-ELM model, which shows that the model proposed in this paper realizes accurate and efficient prediction of the quantity of material demand in the distribution network.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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