發(fā)布時(shí)間：2018-03-25 02:40

  本文選題：數(shù)據(jù)驅(qū)動(dòng)　切入點(diǎn)：室內(nèi)環(huán)境舒適性　出處：《山東建筑大學(xué)》2016年碩士論文

【摘要】：建筑為人們?nèi)粘Ｉ�、學(xué)習(xí)、工作提供了干凈、整潔的環(huán)境。隨著社會(huì)的進(jìn)步、發(fā)展,建筑不僅講究外在藝術(shù)美,更講究內(nèi)部的舒適性和節(jié)能性。但舒適性與能耗有時(shí)存在一定沖突,需要用相應(yīng)的策略進(jìn)行協(xié)調(diào)優(yōu)化。為此,本文提出了一種物聯(lián)網(wǎng)環(huán)境下數(shù)據(jù)驅(qū)動(dòng)的舒適性建模與控制策略。該研究主要涉及以下內(nèi)容：第一,采用統(tǒng)計(jì)方法對建筑用電設(shè)備物聯(lián)網(wǎng)系統(tǒng)收集的環(huán)境數(shù)據(jù)進(jìn)行預(yù)處理,預(yù)處理之后的數(shù)據(jù)作為整個(gè)課題研究的數(shù)據(jù)支撐。第二,提出了一種數(shù)據(jù)驅(qū)動(dòng)的二型模糊集模型的構(gòu)建方法。與傳統(tǒng)模糊方法相比,該方法中的二型模糊集理論處理不確定性的能力顯著增強(qiáng)。將該方法應(yīng)用于室內(nèi)環(huán)境舒適性控制中,提出了基于熱舒適性模型的室內(nèi)環(huán)境調(diào)控策略。通過仿真實(shí)驗(yàn)證明了該策略對于特定房間內(nèi)的熱舒適性建模的有效性。第三,提出了一種數(shù)據(jù)驅(qū)動(dòng)的多變量模糊系統(tǒng)模型的構(gòu)建方法,該方法可以有效地解決傳統(tǒng)模糊系統(tǒng)通常面對的規(guī)則爆炸問題。應(yīng)用提出的單輸入規(guī)則模塊連接的神經(jīng)模糊系統(tǒng)對特定房間內(nèi)的冷熱負(fù)荷預(yù)測,仿真結(jié)果證明了所提出的方法對于能耗預(yù)測問題的有效性和優(yōu)越性。第四,針對高舒適與低能耗的追求目標(biāo),提出了一種多目標(biāo)優(yōu)化策略。該策略采用具有強(qiáng)搜索能力的遺傳算法試圖得到一組最優(yōu)解集(又稱Pareto最優(yōu)解集),最后根據(jù)先驗(yàn)知識(shí)及實(shí)際應(yīng)用背景提供一個(gè)最優(yōu)解用于平衡相互矛盾的目標(biāo)。
[Abstract]:Architecture provides a clean and tidy environment for people's daily life, study and work. With the progress and development of society, architecture not only stresses the beauty of external art, More attention is paid to internal comfort and energy saving. However, there are some conflicts between comfort and energy consumption, which need to be coordinated and optimized with corresponding strategies. This paper presents a data-driven comfort modeling and control strategy in the Internet of things environment. The main contents of this study are as follows: firstly, the environmental data collected by the Internet of things system of building electrical equipment are preprocessed by statistical method. The pre-processed data is used as the data support of the whole subject. Secondly, a data-driven fuzzy set model is proposed. The ability of the second type fuzzy set theory to deal with uncertainty is greatly enhanced. This method is applied to indoor environment comfort control. The indoor environment control strategy based on thermal comfort model is proposed. The simulation results show that the strategy is effective for the thermal comfort modeling in a specific room. Third, In this paper, a data-driven multivariable fuzzy system model is proposed. This method can effectively solve the problem of rule explosion in traditional fuzzy system. The proposed neurofuzzy system with single input rule module is used to predict the heat and cold load in a particular room. The simulation results show that the proposed method is effective and superior to the problem of energy consumption prediction. Fourth, aiming at the goal of high comfort and low energy consumption, A multi-objective optimization strategy is proposed, in which a set of optimal solutions (also known as Pareto optimal solution set) is obtained by using a genetic algorithm with strong searching ability, and an optimal solution set is provided according to prior knowledge and practical application background. Solutions are used to balance conflicting goals.
【學(xué)位授予單位】：山東建筑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TU111.195;TP391.44;TN929.5

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