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The precise prediction of irradiation is the critical factor of predicting the output power of photovoltaic power generation system, yet the irradiation vary from region to region because of the different latitude, weather types and altitude etc. Because complicated meteorological data in currently research about the forecast of short-term irradiation is hard to be collected, this paper proposes a simple method for short-term prediction of irradiance only using the meteorological data which easy to obtain. In consideration of the unique geographical location of Wuhan, the weather is classified to four types. The input variables are temperature, irradiance measured by real-time environmental monitoring and solar elevation angle at different times. Then the irradiation is calculated per hour from 05:[KG-*4]00 to 20:[KG-*4]00 using the multivariate backward feedback neural network model. Contrasting the result obtained from the model mentioned above and that only taking historical irradiation data as input, the former has a better outcome. At last, the forecasting skills is calculated based on the Persistence Method. The final results show that all the skills of the proposed model are greater than 0.75, almost distributed between 0.80 and 0.85 for the weather type A and B, which demonstrates the model proposed in this paper is capable of predicting short-term irradiation accurately based on the meteorological information which easy to be obtained.