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Energy Management System for a Residential Positive Energy District Based on Fuzzy Logic Approach (RESTORATIVE) 基于模糊逻辑方法的正能量住宅区能源管理系统 (RESTORATIVE)
IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-07-16 DOI: 10.3390/smartcities7040070
Tony Castillo-Calzadilla, Jesús Oroya-Villalta, Cruz E Borges
There is a clear European Strategy to transition by 2050 from a fossil fuel-based economy to a completely new system based on renewable energy resources, with electricity as the main energy carrier. Positive Energy Districts (PEDs) are urban areas that produce at least as much energy as their yearly consumption. To meet this objective, they must incorporate distributed generation based on renewable systems within their boundaries. This article considers the fluctuations in electricity prices and local renewable availability and develops a PED model with a centralised energy storage system focused on electricity self-sufficiency and self-consumption. We present a fuzzy logic-based energy management system which optimises the state of charge of the energy storage solution considering local electricity production and loads along with the contracted electric tariff. The methodology is tested in a PED comprising 360 households in Bilbao (a city in the north of Spain), setting various scenarios, including changes in the size of the electric storage, long-term climate change effects, and extreme changes in the price of energy carriers. The study revealed that the assessed PED could reach up to 75.6% self-sufficiency and 76.8% self-consumption, with climate change expected to improve these values. On economic aspects, the return on investment of the proposal ranges from 6 up to 12 years depending on the configuration choice. Also, the case that boosts the economic viability is tight to non-business as usual (BaU), whichever event spiked up the prices or climate change conditions shortens the economic variables. The average bill is around 12.89 EUR/month per house for scenario BaU; meanwhile, a catastrophic event increases the bill by as much as 76.7%. On the other hand, climate crisis events impact energy generation, strengthening this and, as a consequence, slightly reducing the bill by up to 11.47 EUR/month.
欧洲有一项明确的战略,即到 2050 年,从以化石燃料为基础的经济过渡到以可再生能源为基础、以电力为主要能源载体的全新系统。正能量区(PEDs)是指所生产的能源至少与其年消耗量相当的城市区域。为实现这一目标,它们必须在其范围内采用基于可再生能源系统的分布式发电。本文考虑了电价波动和当地可再生能源的可用性,并开发了一个带有集中储能系统的 PED 模型,其重点是电力自给自足和自我消费。我们提出了一种基于模糊逻辑的能源管理系统,该系统可在考虑当地电力生产和负荷以及合同电价的情况下,优化储能解决方案的充电状态。该方法在毕尔巴鄂(西班牙北部城市)的一个由 360 户家庭组成的 PED 中进行了测试,设置了各种情景,包括储电规模的变化、长期气候变化的影响以及能源载体价格的极端变化。研究结果表明,经评估的 PED 自给率可达 75.6%,自耗率可达 76.8%,预计气候变化将提高这些数值。在经济方面,该建议的投资回报期从 6 年到 12 年不等,具体取决于配置选择。此外,提高经济可行性的情况与 "一切照旧"(BaU)紧密相关,无论价格上涨还是气候变化都会缩短经济变量。在 "一切照旧 "的情况下,每栋房屋的平均费用约为每月 12.89 欧元;而灾难性事件则会使费用增加 76.7%。另一方面,气候危机事件会影响能源生产,加强能源生产,从而使账单略有减少,最多为 11.47 欧元/月。
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引用次数: 0
Enhancing Property Valuation in Post-War Recovery: Integrating War-Related Attributes into Real Estate Valuation Practices 加强战后恢复中的房地产估价:将战争相关属性纳入房地产估价实践
IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-07-05 DOI: 10.3390/smartcities7040069
Mounir Azzam, Valerie Graw, Eva Meidler, A. Rienow
In post-war environments, property valuation encounters obstacles stemming from widespread destruction, population displacement, and complex legal frameworks. This study addresses post-war property valuation by integrating war-related considerations into the ISO 19152 Land Administration Domain Model, resulting in a valuation information model for Syria’s post-war landscape, serving as a reference for property valuation in conflict-affected areas. Additionally, property valuation is enhanced through visualization modeling, aiding the comprehension of war-related attributes amidst and following conflict. We utilize data from a field survey of 243 Condominium Units in the Harasta district, Rural Damascus Governorate. These data were collected through quantitative interviews with real estate companies and residents to uncover facts about property prices and war-related conditions. Our quantitative data are analyzed using inferential statistics of mean housing prices to assess the impact of war-related variables on property values during both wartime and post-war periods. The analysis reveals significant fluctuations in prices during wartime, with severely damaged properties experiencing notable declines (about −75%), followed by moderately damaged properties (about −60%). In the post-war phase, rehabilitated properties demonstrate price improvements (1.8% to 22.5%), while others continue to depreciate (−55% to −65%). These insights inform post-war property valuation standards, facilitating sustainable investment during the post-war recovery phase.
在战后环境中,财产估值会遇到广泛破坏、人口流离失所和复杂法律框架带来的障碍。本研究通过将战争相关因素纳入 ISO 19152 土地管理域模型来解决战后财产估值问题,从而为叙利亚的战后景观建立了一个估值信息模型,为受冲突影响地区的财产估值提供参考。此外,还通过可视化建模加强了财产估值,有助于理解冲突中和冲突后与战争相关的属性。我们利用了对大马士革农村省哈拉斯塔区 243 个共有公寓单位的实地调查数据。这些数据是通过对房地产公司和居民的定量访谈收集的,目的是揭示有关房地产价格和战争相关情况的事实。我们使用平均房价的推断统计法对定量数据进行分析,以评估战争相关变量在战时和战后对房产价值的影响。分析结果显示,战时房价波动明显,受损严重的房产价格下降明显(约-75%),其次是受损程度一般的房产(约-60%)。在战后阶段,修复后的房产价格有所提高(1.8% 到 22.5%),而其他房产则继续贬值(-55% 到 -65%)。这些见解为战后房产估价标准提供了参考,有利于战后恢复阶段的可持续投资。
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引用次数: 0
Human-Centric Collaboration and Industry 5.0 Framework in Smart Cities and Communities: Fostering Sustainable Development Goals 3, 4, 9, and 11 in Society 5.0 智能城市和社区中以人为中心的协作与工业 5.0 框架:在社会 5.0 中促进可持续发展目标 3、4、9 和 11
IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-07-05 DOI: 10.3390/smartcities7040068
Amr Adel, Noor HS Alani
The necessity for substantial societal transformations to meet the Sustainable Development Goals (SDGs) has become more urgent, especially in the wake of the COVID-19 pandemic. This paper examines the critical role of disruptive technologies, specifically Industry 5.0 and Society 5.0, in driving sustainable development. Our research investigation focuses on their impact on product development, healthcare innovation, pandemic response, and the development of nature-inclusive business models and smart cities. We analyze how these technologies influence SDGs 3 (Good Health and Well-Being), 4 (Quality Education), 9 (Industry, Innovation, and Infrastructure), and 11 (Sustainable Cities and Communities). By integrating these concepts into smart cities, we propose a coordinated framework to enhance the achievement of these goals. Additionally, we provide a SWOT analysis to evaluate this approach. This study aims to guide industrialists, policymakers, and researchers in leveraging technological advancements to meet the SDGs.
为实现可持续发展目标(SDGs)而进行重大社会变革的必要性已变得更加迫切,尤其是在 COVID-19 大流行之后。本文探讨了颠覆性技术(特别是工业 5.0 和社会 5.0)在推动可持续发展方面的关键作用。我们的研究调查侧重于它们对产品开发、医疗保健创新、流行病应对以及自然包容性商业模式和智慧城市发展的影响。我们分析了这些技术如何影响可持续发展目标 3(良好的健康和福祉)、4(优质教育)、9(工业、创新和基础设施)和 11(可持续城市和社区)。通过将这些概念融入智慧城市,我们提出了一个协调框架,以促进这些目标的实现。此外,我们还提供了 SWOT 分析来评估这种方法。本研究旨在指导工业家、决策者和研究人员利用技术进步实现可持续发展目标。
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引用次数: 0
Data-Driven Reliability Prediction for District Heating Networks 数据驱动的区域供热网络可靠性预测
IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-07-02 DOI: 10.3390/smartcities7040067
Lasse Kappel Mortensen, H. Shaker
As district heating networks age, current asset management practices, such as those relying on static life expectancies and age- and rule-based approaches, need to be replaced by data-driven asset management. As an alternative to physics-of-failure models that are typically preferred in the literature, this paper explores the application of more accessible traditional and novel machine learning-enabled reliability models for analyzing the reliability of district heating pipes and demonstrates how common data deficiencies can be accommodated by modifying the models’ likelihood expressions. The tested models comprised the Herz, Weibull, and the Neural Weibull Proportional Hazard models. An assessment of these models on data from an actual district heating network in Funen, Denmark showed that the relative youth of the network complicated the validation of the models’ distributional assumptions. However, a comparative evaluation of the models showed that there is a significant benefit in employing data-driven reliability modeling as they enable pipes to be differentiated based on the their working conditions and intrinsic features. Therefore, it is concluded that data-driven reliability models outperform current asset management practices such as age-based vulnerability ranking.
随着区域供热网络的老化,目前的资产管理实践,如依赖于静态预期寿命和基于年龄和规则的方法,需要被数据驱动的资产管理所取代。作为文献中通常首选的物理失效模型的替代方案,本文探讨了如何应用更易于使用的传统和新型机器学习可靠性模型来分析区域供热管道的可靠性,并展示了如何通过修改模型的似然表达式来解决常见的数据缺陷。测试的模型包括赫兹模型、Weibull 模型和神经 Weibull 比例危险模型。通过对丹麦富能实际区域供热网络的数据对这些模型进行评估发现,网络的相对年轻化使得模型分布假设的验证变得复杂。不过,对模型的比较评估表明,采用数据驱动的可靠性模型有很大好处,因为这些模型可以根据管道的工作条件和内在特征对其进行区分。因此,得出的结论是,数据驱动的可靠性模型优于当前的资产管理方法,如基于年龄的脆弱性排序。
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引用次数: 0
Personalization of the Car-Sharing Fleet Selected for Commuting to Work or for Educational Purposes—An Opportunity to Increase the Attractiveness of Systems in Smart Cities 为上下班通勤或教育目的而选择的汽车共享车队的个性化--提高智能城市中系统吸引力的机遇
IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-07-02 DOI: 10.3390/smartcities7040066
K. Turoń
Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development.
在城市中心提供短期车辆租赁的汽车共享服务正在全球迅速扩张,但也面临着诸多挑战。其中一个重大挑战是如何有效管理车队的选择,以满足用户的期望。针对这一挑战以及方法论和文献方面的空白,本文旨在提出一种独创的方法,以支持对汽车共享系统中使用的车队的适用性进行评估,并确定特定类型出行所需的用户偏好的车辆特征。所提出的方法结合了交通系统建模和并行分析的要素,并通过一项涉及汽车共享服务运营商的实际案例研究进行了测试。研究的重点是汽车共享用户出于工作或教育目的的通勤需求。研究对象是柏林的一家德国汽车共享运营商。研究时间为 2022 年 1 月 1 日至 6 月 30 日。调查结果表明,受访者最青睐的车辆是 D 级或 E 级大型汽车,配备功率为 63 至 149 千瓦的内燃机,至少配备停车传感器、导航、免提、车道辅助、加热座椅,以及欧洲 NCAP 评级所显示的高安全标准,并以尽可能低的租金提供。这些结果与德国的市场趋势相吻合,德国市场的重点是销售至少中型车。这表明小型汽车在汽车共享系统中的局限性,而小型汽车在意识形态上本应是此类服务的主要车队。所开发的方法既能帮助系统运营商验证其车队是否满足用户需求,也能帮助城市决策者有效管理汽车共享服务政策,包括车队组成、定价法规和车辆设备标准。这项工作是在智能城市背景下提高汽车共享服务效率的重要一步,在智能城市中,个性化和优化交通对可持续发展至关重要。
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引用次数: 0
Business Models Used in Smart Cities—Theoretical Approach with Examples of Smart Cities 智慧城市中使用的商业模式--结合智慧城市实例的理论方法
IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-07-01 DOI: 10.3390/smartcities7040065
R. Wolniak, Bożena Gajdzik, Michaline Grebski, Roman Danel, W. Grebski
This paper examines business model implementations in three leading European smart cities: London, Amsterdam, and Berlin. Through a systematic literature review and comparative analysis, the study identifies and analyzes various business models employed in these urban contexts. The findings reveal a diverse array of models, including public–private partnerships, build–operate–transfer arrangements, performance-based contracts, community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies. Each city leverages a unique combination of these models to address its specific urban challenges and priorities. The study highlights the role of PPPs in large-scale infrastructure projects, BOT arrangements in transportation solutions, and performance-based contracts in driving efficiency and accountability. It also explores the benefits of community-centric models, innovation hubs, revenue-sharing models, outcome-based financing, and asset monetization strategies in enhancing the sustainability, efficiency, and livability of smart cities. The paper offers valuable insights for policymakers, urban planners, and researchers seeking to advance smart city development worldwide.
本文研究了欧洲三个主要智慧城市的商业模式实施情况:伦敦、阿姆斯特丹和柏林。通过系统的文献综述和比较分析,本研究确定并分析了这些城市采用的各种商业模式。研究结果揭示了一系列不同的模式,包括公私合作伙伴关系、建设-运营-转让安排、基于绩效的合同、以社区为中心的模式、创新中心、收入共享模式、基于成果的融资以及资产货币化战略。每个城市都利用这些模式的独特组合来应对其特定的城市挑战和优先事项。本研究强调了公私伙伴关系在大型基础设施项目中的作用,BOT 安排在交通解决方案中的作用,以及基于绩效的合同在提高效率和问责制方面的作用。研究还探讨了以社区为中心的模式、创新中心、收入共享模式、基于成果的融资和资产货币化战略在提高智慧城市的可持续性、效率和宜居性方面的益处。本文为决策者、城市规划者和研究人员在全球范围内推进智慧城市发展提供了宝贵的见解。
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引用次数: 0
Optimization of Geothermal Heat Pump Systems for Sustainable Urban Development in Southeast Asia 优化地源热泵系统,促进东南亚城市可持续发展
IF 6.4 Q1 Social Sciences Pub Date : 2024-06-12 DOI: 10.3390/smartcities7030058
Thiti Chanchayanon, S. Chaiprakaikeow, A. Jotisankasa, S. Inazumi
This study examines the optimization of ground source heat pump (GSHP) systems and energy piles for sustainable urban development, focusing on Southeast Asia. GSHPs, which utilize geothermal energy for indoor HVAC needs, offer a sustainable alternative to traditional systems by utilizing consistent subsurface temperatures for heating and cooling. The study highlights the importance of understanding thermal movement within the soil, especially in soft marine clays prevalent in Southeast Asia, to improve GSHP system efficiency. Using a one-dimensional finite difference model, the study examines the effects of soil thermal conductivity and density on system performance. The results show that GSHP systems, especially when integrated with energy piles, significantly reduce electricity consumption and greenhouse gas emissions, underscoring their potential to mitigate the urban heat island effect in densely populated areas. Despite challenges posed by the region’s hot and humid climate, which could affect long-term effectiveness, the study highlights the need for further study, including field experiments and advanced modeling techniques, to optimize GSHP configurations and fully exploit geothermal energy in urban environments. The study’s insights into soil thermal dynamics and system design optimization contribute to advancing sustainable urban infrastructure development.
本研究以东南亚为重点,探讨了优化地源热泵(GSHP)系统和能源堆以促进城市可持续发展的问题。地源热泵利用地热能满足室内暖通空调需求,通过利用稳定的地下温度进行供暖和制冷,为传统系统提供了一种可持续的替代方案。该研究强调了了解土壤内部热运动的重要性,尤其是在东南亚地区普遍存在的软质海相粘土中,以提高 GSHP 系统的效率。该研究使用一维有限差分模型,考察了土壤导热性和密度对系统性能的影响。研究结果表明,GSHP 系统,尤其是与能源桩结合使用时,可显著降低耗电量和温室气体排放量,这也凸显了该系统在缓解人口稠密地区城市热岛效应方面的潜力。尽管该地区炎热潮湿的气候会影响地热能的长期有效性,但这项研究强调了进一步研究的必要性,包括实地实验和先进的建模技术,以优化 GSHP 配置并充分利用城市环境中的地热能。该研究对土壤热动力学和系统优化设计的见解有助于推动城市基础设施的可持续发展。
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引用次数: 0
Artificial Intelligence in Smart Cities—Applications, Barriers, and Future Directions: A Review 智能城市中的人工智能--应用、障碍和未来方向:综述
IF 6.4 Q1 Social Sciences Pub Date : 2024-06-10 DOI: 10.3390/smartcities7030057
R. Wolniak, K. Stecuła
As urbanization continues to pose new challenges for cities around the world, the concept of smart cities is a promising solution, with artificial intelligence (AI) playing a central role in this transformation. This paper presents a literature review of AI solutions applied in smart cities, focusing on its six main areas: smart mobility, smart environment, smart governance, smart living, smart economy, and smart people. The analysis covers publications from 2021 to 2024 available on Scopus. This paper examines the application of AI in each area and identifies barriers, advances, and future directions. The authors set the following goals of the analysis: (1) to identify solutions and applications using artificial intelligence in smart cities; (2) to identify the barriers to implementation of artificial intelligence in smart cities; and (3) to explore directions of the usage of artificial intelligence in smart cities.
随着城市化不断给世界各地的城市带来新的挑战,智慧城市的概念是一个前景广阔的解决方案,而人工智能(AI)在这一转变中发挥着核心作用。本文对应用于智慧城市的人工智能解决方案进行了文献综述,重点关注其六个主要领域:智能交通、智能环境、智能治理、智能生活、智能经济和智能人。分析涵盖 Scopus 上 2021 年至 2024 年的出版物。本文探讨了人工智能在各个领域的应用,并确定了障碍、进展和未来方向。作者设定了以下分析目标:(1)确定在智慧城市中使用人工智能的解决方案和应用;(2)确定在智慧城市中实施人工智能的障碍;以及(3)探索在智慧城市中使用人工智能的方向。
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引用次数: 1
Characterizing Smart Cities Based on Artificial Intelligence 基于人工智能的智慧城市特征描述
IF 6.4 Q1 Social Sciences Pub Date : 2024-06-07 DOI: 10.3390/smartcities7030056
L. Hammoumi, M. Maanan, H. Rhinane
Cities worldwide are attempting to be labelled as smart, but truly classifying as such remains a great challenge. This study aims to use artificial intelligence (AI) to classify the performance of smart cities and identify the factors linked to their smartness. Based on residents’ perceptions of urban structures and technological applications, this study included 200 cities globally. For 147 cities, we gathered the perceptions of 120 residents per city through a survey of 39 questions covering two main pillars: ‘Structures’, referring to the existing infrastructure of the city, and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. These pillars were evaluated across five key areas: health and safety, mobility, activities, opportunities, and governance. For the remaining 53 cities, scores were derived by analyzing pertinent data collected from various online resources. Multiple machine learning algorithms, including Random Forest, Artificial Neural Network, Support Vector Machine, and Gradient Boost, were tested and compared in order to select the best one. The results showed that Random Forest and the Artificial Neural Network are the best trained models that achieved the highest levels of accuracy. This study provides a robust framework for using machine learning to identify and assess smart cities, offering valuable insights for future research and urban planning.
全世界的城市都在试图被贴上智慧城市的标签,但真正将城市划分为智慧城市仍是一个巨大的挑战。本研究旨在利用人工智能(AI)对智慧城市的表现进行分类,并找出与智慧城市相关的因素。根据居民对城市结构和技术应用的看法,这项研究涵盖了全球 200 个城市。在 147 个城市中,我们通过 39 个问题的调查收集了每个城市 120 名居民的看法,这些问题涉及两个主要支柱:"结构",指城市现有的基础设施;"技术 "支柱,描述居民可用的技术供应和服务。这些支柱的评估涉及五个关键领域:健康与安全、流动性、活动、机遇和治理。其余 53 个城市的得分是通过分析从各种在线资源中收集到的相关数据得出的。为了选出最佳算法,对随机森林、人工神经网络、支持向量机和梯度提升等多种机器学习算法进行了测试和比较。结果表明,随机森林和人工神经网络是经过训练的最佳模型,准确率最高。这项研究为使用机器学习识别和评估智慧城市提供了一个强大的框架,为未来的研究和城市规划提供了宝贵的见解。
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引用次数: 1
Effectiveness of the Fuzzy Logic Control to Manage the Microclimate Inside a Smart Insulated Greenhouse 模糊逻辑控制对智能保温温室内部微气候管理的有效性
IF 6.4 Q1 Social Sciences Pub Date : 2024-06-06 DOI: 10.3390/smartcities7030055
Jamel Riahi, Hamza Nasri, Abdelkader Mami, Silvano Vergura
Agricultural greenhouses incorporate intricate systems to regulate the internal climate. Among the crucial climatic variables, indoor temperature and humidity take precedence in establishing an optimal environment for plant production and growth. The present research emphasizes the efficacy of employing intelligent control systems in the automation of the indoor climate for smart insulated greenhouses (SIGs), utilizing a fuzzy logic controller (FLC). This paper proposes the use of an FLC to reduce the energy consumption of a greenhouse. In the first step, a thermodynamic model is presented and experimentally validated based on thermal heat exchanges between the indoor and outdoor climatic variables. The outcomes show the effectiveness of the proposed model in controlling indoor air temperature and relative humidity with a low error percentage. Secondly, several fuzzy logic control models have been developed to regulate the indoor temperature and humidity for cold and hot periods. The results show the good performance of the proposed FLC model as highlighted by the statistical analysis. In fact, the root mean squared error (RMSE) is very small and equal to 0.69% for temperature and 0.23% for humidity, whereas the efficiency factor (EF) of the fuzzy logic control is equal to 99.35% for temperature control and 99.86% for humidity control.
农业温室采用复杂的系统来调节内部气候。在关键的气候变量中,室内温度和湿度在为植物生产和生长创造最佳环境方面占据首要地位。本研究强调智能控制系统在智能保温温室(SIG)室内气候自动化中的功效,利用了模糊逻辑控制器(FLC)。本文建议使用 FLC 来降低温室的能耗。首先,根据室内外气候变量之间的热交换,提出了一个热力学模型并进行了实验验证。结果表明,所提出的模型能有效控制室内空气温度和相对湿度,且误差率较低。其次,还开发了多个模糊逻辑控制模型,用于调节冷热时段的室内温度和湿度。统计分析结果表明,拟议的 FLC 模型性能良好。事实上,均方根误差(RMSE)非常小,温度为 0.69%,湿度为 0.23%,而模糊逻辑控制的效率系数(EF)为 99.35%(温度控制)和 99.86%(湿度控制)。
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引用次数: 0
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Smart Cities
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