基于BIM+Multi-Agent增强学习的智慧建筑及城市运维软件设计

This paper presents the design of a wireless building monitoring network implemented at the University of Nottingham's Creative Energy Homes test site. The network is installed in seven smart buildings with the aim of holistically collecting energy data. Data will be used to inform a central control algorithm to optimise the energy flows between buildings, in turn promoting the smart cities concept. Sensors and meters measuring temperature, humidity, CO2, heat energy, power, and stratified tank temperature are described. Furthermore, the communication protocols utilised are also discussed, which include wireless MBus and EnOcean. This paper also covers the methods used for ensuring the reliability of data signals and the system controls.

Intelligent context-awareness system for energy efficiency in smart building based on ontology

With the flourishing and advancing part of IoT and Cloud Computing, smart cities are becoming an emerging standard, having ubiquitous sensing, high level wireless network infrastructure, consisting of intelligent information processing and control systems. Smart city systems can monitor the real world and real time, and are able to provide smart services and solutions to local residents and travellers in terms of entertainment, healthcare, energy and transportation. But security and privacy concerns are still there as Smart city systems collect a wide range of sensitive data which is directly proportional to the privacy of people. Here in this paper, we have firstly introduced about the key ideas for upcoming enhancements for smart cities and their data management strategies. After that we have discussed many security and privacy challenges while applying for these applications in real world. Mainly, here in we have pointed out some technologies and key ideas which can be researched and implemented in the coming future.

Utilising a sequential mixed-method design to investigate the relationship between planning and smart city development; this thesis aims to work toward an integrated response to these challenges through the provision of an evaluative framework for planning smart cities. Such a framework would act as a medium in recommending a future role and direction for the sector of planning and would aim to build upon the opportunities of technology found in smart cities. Such opportunities include improving the weaknesses of traditional-delivery-methods of development in what the thesis broadly views as an analogue approach to the future of cities challenge in a digital world. The thesis employs a grounded-theory approach to data analysis in phase one, before quantitatively analysing planning policy in phase two and comparing these measurements to questionnaire data in phase three to produce an evaluative framework for the role of planning in smart cities. This linkage via sequential steps for data collection and analysis is reflective of the holistic approach of planning as a profession, whereby a consultative process for expertise in the fields of transport, the environment, heritage and infrastructure combine to form a cohesive system of the built environment; leading this thesis to advocate for an introduction of a digital peer if cities are to be smart about their transformation. In being knowledgeable about this transformation the study’s main findings break down the cycle of data in smart cities to data capture, data communication, data processing and data production; before relating these to smart city development principles that combine with the project phases of planning developments to form quadrants, or areas of opportunity, for planning smart cities – building upon the strengths of planning to improve the capacity of cities in operating throughout the new environment of the digital world.

Real world engineering problems often involve selecting between alternatives. As cyber-physical systems (CPS) become more prevalent in society, including smart cities, intelligent transportation networks, smart homes, etc., these alternatives will increasingly not only include variations in hardware but also in software. Stakeholders will have to decide between competing designs consisting of varying sensor sets and varying machine learning models. Ensuring that these selections are well guided and include multiple objectives such as performance, cost, and sensitivity is paramount. Many of these CPS will include machine learning classification models. The receiver operating characteristic (ROC) analysis is useful for selecting between alternative classification models based on performance and sensitivity, however, this analysis fails to treat classification models as a system of hardware and software, a CPS. Therefore, the ROC analysis ignores additional stakeholder objectives such cost and reliability. This thesis presents methods to extend the ROC analysis to include an additional objective of system cost. The methodology is demonstrated on three real world data sets that include additional cost information. The presented methodology is shown to drastically reduce the stakeholder decision space and guide stakeholder decisions using performance, cost, and sensitivity objectives.

This paper describes that to use AHP analyze the intelligent building system of six large-scale shopping malls in Taiwan, and evaluate the system intelligence, then obtains a sorting. This paper has referred Wong, Li and Lai's paper, proposed the frame and appraisal of the intelligent building system. And use MCDM mode to sort the rankings and improvements for of large-scale shopping malls in Taiwan. MCDM mode is employed, to decide the intelligent attribute related importance and evaluating about the intelligent building system of large-scale shopping malls. Through this method is a not only probe the appraisal of large-scale shopping malls in Taiwan, but also consider the operation benefit by the intelligent building system.

In this essay we wish to present an interdisciplinary study carried out in the framework of two Interreg Italy-Austria projects which were intended to design methodologies, simulation tools and decision support instruments to face energy challenges within the overall Smart City concept. The proposed strategy uses a physical model (“City Model”) and an energy behavior model (“City Sensing”) of the urban environment. They were developed using two different approaches: the first is mainly based on analysis of surveys data and the second one on simulations and processing of large-scale datasets. “City Model” and “City Sensing” were then merged to provide an Urban Building Energy Model - that we called “City Energy Model” - integrated with a Participatory Public GIS platform.

Cities are the most preferable dwelling places,having with better employment opportunities,educational hubs,medical services,recreational facilities,theme parks,and shopping malls etc.Cities are the driving forces for any national economy too.Unfortunately now a days,these cities are producing circa 70%of pollutants,even though they only occupy 2%of surface of the Earth.Public utility services cannot meet the demands of unexpected growth.The filthiness in cities causing decreasing of Quality of Life.In this light our research paper is giving more concentration on necessity of "Smart Cities ",which are the basis for civic centric services.This article is throwing light on Smart Cities and its important roles.The beauty of this manuscript is scribbling Smart Cities " concepts in pictorially.Moreover this explains on " Barcelona Smart City " using Internet of Things Technologies ".It is a good example in urban paradigm shift.Bracelona is like the heaven on the earth with by providing Quality of Life to all urban citizens.The GOD is Interenet of Things.

Risks in Project Finance Initiatives: Current Trends and Future Directions

Smart cities rely on innovative technologies, guidelines, and mechanisms to advance city dwellers’ quality of life (QoL). Notwithstanding the global attention the smart city concept has received within the last decade, studies establishing pragmatic approaches for implementing smart sustainable city strategies in the Gulf region are rare. This study modelled a practical framework for implementing smart sustainable city strategies and megaprojects in one of the Gulf cities. A qualitative research methodology was used to assess smart city strategies from four cities to identify the optimum implementation strategies. This study design framework adopted a case study methodology, the identification of knowledge gaps, data collection and analysis, and interpretations of key findings. Best practices, paramount/relevant stakeholders, main issues/relevant considerations, 15 key performance indicators (KPI), and outputs/deliverables involved in diverse smart sustainable city strategies and project lifecycle phases were identified. The lifecycle phases adopted in the study were (i) conceptualization, (ii) planning/design, and (iii) installation/closure. A pragmatic understanding of how to effectively appraise, monitor, and implement smart sustainable city strategies and megaprojects is provided for policy/decision-makers and built environment experts in Saudi Arabia and globally. The proposed implementation strategic framework can perform the function of an appraisal tool for assessing each phase of the smart city project’s life cycle progress, informing preventions of delays or implementation challenges. This study’s contribution to research knowledge is the development of a model that reveals and illustrates the connections between different phases of smart sustainable city strategies and projects.

Aedes aegypti and Aedes albopictus mosquitoes are responsible for the transmission of diseases such as dengue fever, yellow fever, chikungunya fever, zika virus fever, some of which can cause irreversible central nervous system problems and death. This study investigates what technologies are being used for combatting and monitoring the Aedes mosquitoes and to propose joining these technologies into a single and complete solution using the Smart Cities concept. A search for newscasts on Google and mobile apps in app stores were performed to identify technological solutions for combat to Aedes mosquitoes. Also, a model for joint technology was proposed. Results identified the following technologies: 170 software, two sensors, two drones, one electronic device, ten mosquito traps/lures, seven biological tools, six biotechnologies, and eight chemical formulations. Technological resources and adoption of preventive measures by the population could be a useful method for the mosquito control. Examples include a georeferenced model for identification and examination of larvae, application of chemical/biological products, real-time mapping, sending of educational materials via email or social media for the population, and alerts to health professionals in the zones of combat/risk. In combination, these technologies may indicate a better solution to the current problem.

Smart building retrofitting complemented by solar assisted heat pumps integrated within a self-correcting intelligent building energy management system.

The common approach to model occupants behaviors in buildings is deterministic and consists of assumptions based on predefined fixed schedules or rules. In contrast with the deterministic models, stochastic and agent based (AB) models are the most powerful and suitable methods for modeling complex systems as the human behavior. In this paper, a co-simulation architecture is proposed with the aim of modeling the occupant behavior in buildings by a stochastic-AB approach and implementing an intelligent Building Energy Management System (BEMS). In particular, optimized control logics are designed for smart passive cooling by controlling natural ventilation and solar shading systems to guarantee the thermal comfort conditions and maintain energy performance. Moreover, the effects of occupant actions on indoor thermal comfort are also taken into account. This study shows how the integration of automation systems and passive techniques increases the potentialities of passive cooling in buildings, integrating or replacing the conventional efficiency strategies.

The available Building Energy Management Systems (BEMS), although they contribute to a significant reduction of energy consumption and improvement of the indoor environment, they can only be implemented in new buildings. Their installation in existing buildings is far from being cost effective due to the incompatibility of communication protocols between BEMS designed by various manufacturers and unavoidable modifications for data transmission. On the other hand, current research for energy efficient buildings has proved that although the design and the facilities including BEMS aim to satisfy the thermal and visual comfort plus the air quality demands while minimising the energy needs, they often do not reach their goals due to users interference. Latest trends in designing Intelligent Building Energy Management Systems (IBEMS) offer a Man Machine Interface that could store the users preferences and adapt the control strategy accordingly. The objectives of the present paper are to present the advantages of the use of a man machine interface based on a smart card terminal together with fuzzy control techniques in satisfying the users preferences plus to underline the capabilities that the LON network offers to the design. A fuzzy PID controller is developed to reach the first of the above objectives. The monitoring of the energy consumption along with satisfying the users preferences is achieved by the use of a suitable cost function for the whole system. All the above parameters as well as the cost function are kept between acceptable limits. The overall control system including the cost function is modeled and tested using MATLAB/SIMULINK. The implementation of the control system in an existing building requires interconnection of sensors and actuators installed across the building, is well served by the LonWorks technology due to its high standards and flexibility features.

A building energy management system (BEMS) is a computer-based system designed to monitor and control a building's energy needs. Modern BEMS rely on the sensing and connectivity capabilities of Internet of Things (IoT) technology to intelligently adjust the energy consumption to reduce cost while respecting the consumers' preferences. Increasingly, control decisions are made based on predictions by models trained using supervised machine learning methods, which still requires control policies to be formulated in a rule-based fashion. When using reinforcement learning (RL) instead, control policies are learned by observing the utility in terms of cost and comfort associated with actions such as a change in the heating system's setpoint. The resulting RL-based controllers can capture not only the dynamics of the building and the associated electrical devices, but also fluctuations in electricity prices and user demand, avoiding the need to combine multiple predictive models with tailored control policies. This chapter will provide an overview of RL-based approaches for BEMS. After sketching the taxonomy of general RL methods, we discuss the implications of relying on the individual methods in a BEMS context. Existing work applying RL is presented along the key devices controlled by BEMS systems. Finally, we summarize the state-of-the-art and sketch limitations and open research directions.