Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

Optimal Management of community Demand Response

More than one-third of the electricity produced globally is consumed by the residential sectors [1], with nearly 17% of CO2 emissions, are coming from residential buildings according to reports from 2018 [2] [3]. In order to cope with increase in electricity demand and consumption, while considering the environmental impacts, electricity providers are seeking to implement solutions to help them balance the supply with the electricity demand while mitigating emissions. Thus, increasing the number of conventional generation units and using unreliable renewable source of energy is not a viable investment. That’s why, in recent years research attention has shifted to demand side solutions [4]. This research investigates the optimal management for an urban residential community, that can help in reducing energy consumption and peak and CO2 emissions. This will help to put an agreement with the grid operator for an agreed load shape, for efficient demand response (DR) program implementation. This work uses a framework known as CityLearn [2]. It is based on a Machine Learning branch known as Reinforcement Learning (RL), and it is used to test a variety of intelligent agents for optimizing building load consumption and load shape. The RL agent is used for controlling hot water and chilled water storages, as well as the battery system. When compared to the regular building usage, the results demonstrate that utilizing an RL agent for storage system control can be helpful, as the electricity consumption is greatly reduced when it’s compared to the normal building consumption

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

The State of the Art in Model Predictive Control Application for Demand Response

Demand response programs have been used to optimize the participation of the demand side. Utilizing the demand response programs maximizes social welfare and reduces energy usage. Model Predictive Control is a suitable control strategy that manages the energy network, and it shows superiority over other predictive controllers. The goal of implementing this controller on the demand side is to minimize energy consumption, carbon footprint, and energy cost and maximize thermal comfort and social welfare.  This review paper aims to highlight this control strategy\u27s excellence in handling the demand response optimization problem. The optimization methods of the controller are compared. Summarization of techniques used in recent publications to solve the Model Predictive Control optimization problem is presented, including demand response programs, renewable energy resources, and thermal comfort. This paper sheds light on the current research challenges and future research directions for applying model-based control techniques to the demand response optimization problem

Renewable Energy Approach with Indonesian Regulation Guide Uses Blockchain-BIM to Green Cost Performance

Climate change is a threat and crisis that hit the world today; one of them is causing drought, rising sea levels, melting polar ice, and heat waves; therefore, the target towards Net Zero Emission (NZE) in 2060 must be an obligation in all countries. Green Building (GB) is a building that meets Building Technical Standards, and has demonstrated demonstrable success in conserving resources such as water, energy, and other resources. The application of GB principles following the function and classification in every stage of their implementation is expected to reduce greenhouse gas emissions. This research aims to analyze the cost of improvement work based on GB assessment in applying the Technical Guidelines from Minister of Public Works and Public Housing (PUPR) No. 1 of 2022, which is the latest regulation in Indonesia. The blockchain-BIM method and the implementation of the GB component will be analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS) to find the most influential factors. The results of this study show that by applying Blockchain-BIM to overcome the cost constraints, it is proven to be able to increase the cost performance of GB in modern shopping center buildings by 3–3.8% in the Basic rating, while for other ratings, it is 0.5–2.1% higher, where the selection of a renewable energy model is very influential. Doi: 10.28991/CEJ-2023-09-10-09 Full Text: PD

Designing a Kinetic Fa\c{c}ade Using BB-BC Algorithm with a Focus on Enhancing Building Energy Efficiency

In order to increase energy efficiency in buildings, optimizing the parameters of the facade form can be challenging due to the dynamic nature of solar radiation. One effective solution is the use of kinetic facades as a second skin, which can control energy consumption. This study proposes a parametric kinetic facade to increase building energy efficiency, along with a framework to optimize its form using the Bang-Big Crunch (BB-BC) optimization algorithm. The study involved modeling a two-story office building in Shiraz city and calculating the energy consumption resulting from building operation over a three-day period without considering the second skin of the facade. In the second stage of the study, the second skin was optimized for the same three-day interval and calculated as a parametric, static facade. In the last step, the parameters of the second skin were optimized for three one-day intervals, assuming the possibility of kinematic changes each day. The total energy consumption of building operation for the three days was then calculated and analyzed.The Python programming language was used to develop the optimization algorithm, while Rhino software, and Grasshopper, Ladybug, and Honeybee plugins were used for building modeling and simulation of light, energy, and weather parameters.The results of the study demonstrate the effectiveness of the proposed kinetic facade and the proper performance of the proposed algorithm for solving similar problems. The study found that the use of the second skin with kinetic function reduced energy consumption by 28%. Additionally, the results from the second and third stages of the study showed that the use of the second facade shell with kinematic function, compared to its function in static mode, reduced energy consumption by 4%.Overall...Comment: 8th International conference on Civil Engineering, Architecture & Urban Development,Tehran, Ira

Advancements in Building Deconstruction: Examining the Role of Drone Technology and Building Information Modelling

Deconstructing a building with the help of drones and BIM (building information modelling) is becoming increasingly common as a more efficient, eco-friendly, and affordable alternative to the traditional techniques of building disassembly. This paper presents a systematic review following the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to investigate the role of drone technology and BIM in building deconstruction. A total of 10 studies were identified based on the integration of drone technology with BIM, all of which proved promising in enhancing the process of building deconstruction. The analysis of the 35 and 3 non-academic selected data reveals several key findings. Firstly, BIM is not commonly used in deconstruction or demolition processes, particularly in managing fixtures and fittings of buildings. Secondly, the adoption of deconstruction-oriented design methods and the use of drone technology can significantly reduce the negative environmental impacts of building demolition waste. Lastly, the limited implementation of design for deconstruction practices in the construction industry hinders the realisation of environmental, social, and economic benefits associated with this approach. Overall, this systematic review highlights the potential of drone technology and BIM in improving building deconstruction practices, while also identifying knowledge gaps and areas for further research and development on this topic

USER SATISFACTION WITH REGENERATIVE ARCHITECTURE PRINCIPLES IN SELECTED RECREATIONAL CENTRES IN LAGOS, NIGERIA

Buildings should contribute to and renew their environment rather than drain them, much as plants and animals do when they adapt to their surroundings and participate in maintaining the ecosystem. Therefore, this study aimed to assess user satisfaction and perceptions of regenerative architecture in recreational centres, focusing on how these principles inform user-centred design and promote sustainable development while identifying areas for improvement. The research employed a two-fold approach, commencing with a theoretical study of regenerative architecture, followed by a quantitative method involving the distribution of structured questionnaires. These questionnaires sought to gather information from users within the study area, focusing on their satisfaction and perceptions regarding various aspects of regenerative architecture principles implemented in the recreational centres. A total of 120 questionnaires were distributed to the users of the recreational facilities, and the return rate was 87.5%. Purposive and random sampling techniques were used to select the recreational centres and the respondents respectively. The responses were analysed using a Statistical Package for the Social Sciences (SPSS) software. The study reveals a generally positive perception of regenerative architecture principles in the recreational centres, with users expressing satisfaction in various aspects related to the green spaces, design interaction, cultural expression visual appeal, air quality, and maintenance of the recreational centres

Transforming Smart Cities with Artificial Intelligence: Opportunities, Challenges, and Future Implications

This paper explores the impact of artificial intelligence (AI) on smart cities. With the rapid development of AI, its applications in smart cities have become increasingly important in enhancing urban development, improving public services, and creating sustainable and efficient urban environments. The paper first provides an overview of smart cities and AI, highlighting the importance of studying the impact of AI on smart cities. It then examines the role of AI in smart cities, including its definition, applications, and benefits. The paper also analyzes the impact of AI on smart city development, including changes in urban planning and design, transportation and traffic management, energy efficiency, and public safety and security. However, the potential risks and challenges of AI in smart cities, such as ethical and privacy concerns, job displacement, and cybersecurity risks, are also discussed. Finally, the paper explores the future of AI in smart cities, including opportunities for further innovation, collaboration between public and private sectors, and potential impact on urban lifestyles and citizen engagement. The paper concludes with a summary of the key points and implications for future research and policy-making

Grafický detail pro BIM ve virtuální a rozšířené realitě

The subject of the diploma thesis is to create a Building Information Model in graphic detail in the phase of use, based on the available 3D drawing documentation of the new building and try to analyse the model using the Navisworks Manage software to try out the BIM tools like clash detection and time liner and try establishing the model in VR and AR environment.The subject of the diploma thesis is to create a Building Information Model in graphic detail in the phase of use, based on the available 3D drawing documentation of the new building and try to analyse the model using the Navisworks Manage software to try out the BIM tools like clash detection and time liner and try establishing the model in VR and AR environment.222 - Katedra městského inženýrstvívýborn