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A fusion prediction method was proposed to predict and correct the calculation deviation of the top transformer oil temperature model in IEEE guideline，so as to realize the more precise prediction of the transformer top oil temperature（TOT）.Firstly，the characteristics of the transformer TOT model and the extreme learning machine（ELM） prediction model was introduced. Secondly，in order to avoid the problem of slow operation speed caused by double level intelligent prediction，the weighted multi-point extrapolation method combined with the load curve clustering algorithm was used to obtain the future load coefficient of the transformer which introduced as the load prediction level of the model. Finally，based on the calculation of thermal model，which the ELM was used to predict the deviation between the calculated value of thermal model and the measured value，and finally the accurate predicted value of the TOT of the transformer was obtained.The simulation platform was built and the simulation results show that the average prediction error rate of the proposed prediction method is only 0.59%，and the root mean square error is only 0.47 ℃. Compared with the other three methods，it has higher prediction accuracy and stability. The model training speed and prediction speed are only 1.21 ms and 0.39 ms，respectively，which proves that the fusion prediction model proposed and established has high prediction accuracy，stability and operation speed.
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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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