【摘要】：隨著生活水平的不斷提高，人們對(duì)生活品質(zhì)的要求也愈來愈高。在現(xiàn)代生活中，人類的工作、娛樂、生活等大部分時(shí)間均處于室內(nèi)，因此，人們對(duì)室內(nèi)環(huán)境品質(zhì)的需求也越來越高。為順應(yīng)人們對(duì)舒適、節(jié)能、健康的室內(nèi)環(huán)境的追求，，本文對(duì)室內(nèi)環(huán)境熱舒適度預(yù)測(cè)建模與室內(nèi)環(huán)境熱舒適度控制在空調(diào)系統(tǒng)中的應(yīng)用做了相應(yīng)研究。 針對(duì)熱舒適度預(yù)測(cè)是一個(gè)復(fù)雜的非線性過程，不便于空調(diào)的實(shí)時(shí)控制應(yīng)用的問題，本文提出一種改進(jìn)的粒子群算法(PSO)優(yōu)化反向傳播(BP)神經(jīng)網(wǎng)絡(luò)的熱舒適度預(yù)測(cè)模型。這一預(yù)測(cè)模型通過采用PSO算法優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的初始權(quán)值和閾值，改善了傳統(tǒng)BP算法收斂速度慢及對(duì)網(wǎng)絡(luò)初始值敏感的問題。同時(shí)，本文針對(duì)標(biāo)準(zhǔn)PSO算法易出現(xiàn)早熟收斂、局部尋優(yōu)能力弱等缺點(diǎn)，提出了相應(yīng)改進(jìn)策略，進(jìn)一步提高了PSO優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的能力。實(shí)驗(yàn)結(jié)果表明：基于改進(jìn)的PSO-BP算法的熱舒適度預(yù)測(cè)模型較傳統(tǒng)BP模型和標(biāo)準(zhǔn)PSO-BP模型，具有預(yù)測(cè)精度高且算法收斂速度快的特點(diǎn)。 本文針對(duì)室內(nèi)環(huán)境熱舒適度控制在空調(diào)系統(tǒng)中應(yīng)用實(shí)現(xiàn)的問題，做了控制變量、控制方式、控制算法等分析比較研究，并最終確定以溫度與風(fēng)速作為系統(tǒng)中的控制變量，以熱舒適度的直接控制方式結(jié)合智能模糊控制算法實(shí)現(xiàn)室內(nèi)環(huán)境的熱舒適度控制。同時(shí)，本文通過對(duì)模糊控制器的設(shè)計(jì)步驟與設(shè)計(jì)要點(diǎn)的研究，設(shè)計(jì)了熱舒適度模糊控制器，并對(duì)空調(diào)系統(tǒng)的熱舒適度模糊控制器進(jìn)行了仿真實(shí)現(xiàn)。仿真結(jié)果表明，本文設(shè)計(jì)的模糊控制器性能比傳統(tǒng)PID控制器更佳，并且采用熱舒適度模糊控制的空調(diào)系統(tǒng)比傳統(tǒng)溫度控制的空調(diào)系統(tǒng)能進(jìn)行更好的熱舒適度控制，并能提供更舒適度的室內(nèi)環(huán)境。
[Abstract]:With the continuous improvement of living standards, people's requirements for the quality of life are becoming higher and higher. In modern life, people's work, entertainment, life and so on most of the time are in the indoor, therefore, people's demand for indoor environmental quality is also increasing. In order to adapt to people's pursuit of comfortable, energy saving and healthy indoor environment, this paper studies the prediction modeling of indoor thermal comfort and the application of indoor thermal comfort control in air conditioning system. To solve the problem that thermal comfort prediction is a complex nonlinear process and is not convenient for the real-time control of air conditioning, an improved particle swarm optimization algorithm (PSO) is proposed to optimize the thermal comfort prediction model of backpropagation (BP) neural network. By using PSO algorithm to optimize the initial weights and thresholds of BP neural networks, this prediction model improves the problems of slow convergence speed and sensitivity to the initial network values of the traditional BP algorithm. At the same time, aiming at the shortcomings of standard PSO algorithm, such as premature convergence and weak local optimization ability, the corresponding improvement strategy is proposed, which further improves the ability of PSO to optimize BP neural network. The experimental results show that the thermal comfort prediction model based on the improved PSO-BP algorithm is more accurate than the traditional BP model and the standard PSO-BP model. In order to solve the problem of indoor thermal comfort control applied in air conditioning system, this paper analyzes and compares the control variables, control methods and control algorithms, and finally determines the temperature and wind speed as the control variables in the system. The thermal comfort control of indoor environment is realized by direct control of thermal comfort and intelligent fuzzy control algorithm. At the same time, through the study of the design steps and key points of the fuzzy controller, the thermal comfort fuzzy controller is designed, and the simulation of the thermal comfort fuzzy controller of the air conditioning system is carried out. The simulation results show that the performance of the fuzzy controller designed in this paper is better than that of the traditional PID controller, and the thermal comfort fuzzy control system can perform better thermal comfort control than the traditional temperature control system. And can provide a more comfortable indoor environment.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP18;TM925.12

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