Greenhouse energy forecasting method based on hybrid optimization algorithm

Abstract

The invention discloses a greenhouse energy forecasting method based on a hybrid optimization algorithm. The greenhouse forecasting method based on the hybrid optimization algorithm comprises the following steps that (1), a differential equation of temperature inside a greenhouse is set; (2), parameters are initialized; (3), a population is initialized, and the initial values of the parameters needing recognizing are generated randomly; (4), gen is made to be 1; (5), if gen is smaller than or equal to gens_max, the step (6) is carried out, and if gen is greater than gens_max, the step (15) is carried out; (6), k is made to be 1; (7), if k is smaller than or equal to max_k, the step (8) is carried out, or the step (10) is carried out; (8), a current optimal solution and a globally optimal solution are obtained; (9), k is made to be k+1, and the step (7) is carried out again; (10), pop_size grains are selected by utilization of a preferred function; (11), information of reserved M grains is used for regenerating a population of the GA; (12), the grains obtained in the step (11) are used for intersection and variation of the GA; (13), the pop_size-M grains obtained by the GA and the reserved M grains of the PSO are combined to be pop_size new populations; (14), gen is made to be gen+1, and then the step (5) is carried out; (15), the minimum fitness function value and the parameters are output finally, and the forecast energy value of the greenhouse is output.

Description

technical field [0001] The invention belongs to the technical field of agricultural greenhouse environment design and control, and is applicable to the field of greenhouse environment energy prediction where it is difficult to establish an accurate mathematical model. Specifically, it involves an adaptive particle swarm optimization algorithm ( Particle Swarm Optimization , hereinafter referred to as PSO ) and genetic algorithm ( Genetic Algorithms , hereinafter referred to as GA ) method for greenhouse energy prediction using a hybrid optimization algorithm. Background technique [0002] Facility agriculture is one of the key industries of modern agriculture, a symbol of a country's agricultural development level, and an inevitable requirement for high-yield, high-quality, and efficient agriculture. As an important part of facility agricultural production, greenhouse production industry has gradually attracted people's attention. [0003] Greenhouse is a complex sy...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the lack of accuracy and time-consuming of the existing greenhouse energy prediction methods, the present invention provides a greenhouse energy prediction method based on a hybrid optimization algorithm of adaptive PSO and GA

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