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Abstract

Agricultural machinery total power is an important index to reflect and evaluate the level of agricultural mechanization. Firstly, we respectively made use of exponential model, grey forecasting and BP neural network to construct models depending on historical data of agricultural machinery total power of Heilongjiang Province; secondly, we constructed the combined forecasting models that respectively based on divergence coefficient method, quadratic programming and weight distribution of Shapley value. Fitting results showed that the various combination forecasting model is superior to the single models. Finally, we applied the combination forecasting model which based on the weight distribution method of Shapley value to forecast Heilongjiang agricultural machinery total power, and it would provide some reference to the development and program for power of agriculture machinery.

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