Intelligent Integrated Modeling And Optimization Control Of Greenhouse Environmental Systems For Energy Conservation

The primary problem of energy conservation control in greenhouse is how tobuild a greenhouse energy model. Crops, the protagonist of greenhouse,are alive,and it is difficult to detect its physiological processes and ecological processes.Environment affects the growth of greenhouse plants, and the physiological role ofplants,such as transpiration, photosynthesis, respiration, etc., affects thegreenhouse environment smultaneously. So,it is difficult to control the greenhouseenvironment. Greenhouse production process is complicated,with nonlinear,time-varying, strong coupling, multi-interference, uncertainty, imprecision, serioustime lag, and the energy transfer relationship in each part of the greenhouse ismore complex. To solve this problem, it is nessary to find a more effective methodfor modeling, used for energy conservation of the greenhouse.In this paper, the intelligent integrated modeling approach is explored to buildenergy consumption model of the greenhouse for energy conservation. Optimalcontrol strategies for greenhouse environment is explored by controlling thegreenhouse environment factors so that plants grows well in the greenhouse andenergy consumption reduces.The following aspects in the intelligent integrated modeling and intelligentintegrated optimization control of greenhouse is researched or investigated:1. Modeling methods:Conditional entropy-based intelligent integration of grey prediction modelingis proposed which can effectively deal with the plant-specific features dfficult tobe detected such as the physiological response and ecological processes of thegreenhouse plants.Process neural network model of the greenhouse environment system isestablished,which can use both the real-time data and the historical data of thegreenhouse, overcoming the limitations of traditional neural networks such aslarge sample study and generalization.Information entropy-based intelligent integrated optimized model parametersidentification is proposed, making full use of the more valuable information ingreenhouse data.Grey prediction compensation-based mechanism model of the greenhouse environment is established to compensate effectively the uncertainty, imprecision,and more disturbing of the greenhouse and the crop physiological responses andecological processes difficult to detected.Intelligent integrated modeling ideas based on information entropy isproposed to use more valuable information in each single model approach.Self-correcting generalized variance least squares identification model, neuralnetwork model, least squares support vector machines model are established forthe central air conditioning system of closed greenhouse. On the basis of the abovethree models,Intelligent integration model based on immune optimizationalgorithm is established with Satisfactory.Modeling idea and modeling methods described above works well with goodresults: mean absolute error is0.166330255, mean relative error is-0.12%,meanRMSE is0.453116572,mean RE is3.72%,the correlation coefficient betweenForecast data of the model and measured data of greenhouse is0.9331535~0.97287241, The coefficient of determination between them is0.870858467~0.952664962. These indicators show that the proposed intelligentintegrated modeling of the greenhouse is effective and accurate.2. Optimized control strategy:An energy-optimized intelligent integrated control strategy based on TOPSISis proposed, which do not put energy as the only evaluation, but consider a varietyof factors such as energy consumption,environmental conditions,algorithmiterations, etc, meeting the requirements of a number of evaluation fully, to obtainan energy-optimized control strategy with good comprehensive benefits.By using the TOPSIS strategies to integrate genetic algorithms, particleswarm optimization, the standard simulated annealing algorithm, an improvedsimulated annealing algorithm, improved simulated annealing algorithm two toachieve the purpose of saving energy,which is used in glass greenhouse for energysaving.Integration algorithm by the constitution of genetic algorithms, simulatedannealing algorithm, the improved simulated annealing algorithm based on theTOPSIS strategy is used in closed greenhouse.Predatory search algorithm, tabu search algorithm, an improved simulatedannealing algorithm, the standard particle swarm optimization, improved particle swarm optimization is applied to multiple chillers for the purpose of energysaving.The saving rates of single intelligent optimization control strategy when usedin greenhouse is minimum10.80%,maximum35.75%,average20.93%. The savingrates of all these intelligent integrated control strategy above-mentioned are:minimum of37.32%,maximum of44.19%, with an average of40.67%while usedin greenhouse.3.Optimization algorithms:The above variety of standard intelligent optimization algorithms has used inthe greenhouse for the purpose of energy conservation.Two improved simulation algorithm were proposed, standard particle swarmoptimization algorithm was improved.The above mentioned intelligent optimization algorithms and improvedalgorithm is used in the greenhouse for energy saving and emission reduction.4. Other aspects:The statistical theory and methods were used in the field of modeling andcontrol, the forecast data and measured data were statistically analyzed. Emissionswere analyzed for each intelligent integrated optimization control strategy.Economic analysis and mitigation analysis had studied for the greenhouseheating means of conventional coal-fired and ground source heat pump.