Abstract

Based on the comparison and observations of the internal and external meteorological conditions of the heliogreenhouse, BP neural network, stepwise regression and energy balance principle were used, respectively, to construct the greenhouse air temperature prediction model. The results showed that although the BP neural network-based prediction model had a high forecasting accuracy, due to the comparatively different growth characteristics of cultivated crops, there was a lack of wide adaptability of services; the mechanism of the prediction model constructed by the energy balance principle was strong, but it was difficult to obtain the relevant parameters and had a poor prediction accuracy and a short effective service period; the greenhouse temperature prediction model constructed by the stepwise regression had a comparative advantage over the previous two models and the forecasting effectiveness can be for the next 1-7 days.

Keywords:

Air temperature, forecast, heliogreenhouse, model,

References

Competing interests

The authors have no competing interests.

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The authors have no competing interests.