VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY LÊ VĂN TRỌNG ENERGY AND RESOURCE OPTIMIZATION IN BUILDING SMART CITY USING MULTIVERSE OPTIMIZER (MOV) ALGORITHM Major: CONSTRUCTION MANAGMENT Major code: 8580302 MASTER’S THESIS HO CHI MINH CITY, July 2023 THIS THESIS IS COMPLETED AT HO CHI MINH UNIVERSITY OF TECHNOLOGY – VNU – HCM CITY Supervisor: Associate Prof. Pham Vu Hong Son Examiner 1: Dr. Nguyen Anh Thu Examiner 2: PhD. Nguyen Van Tiep This master’s thesis is defended at HCM city University of Technology, VNU-HCM City on 12th, July, 2023.
Master’s Thesis Committee: 1. Chairman Associate Prof. Do Tien Sy 2. Secretary Associate Prof.
Luong Duc Long 3. Nguyen Anh Thu 4. Nguyen Van Tiep 5. Nguyen Thanh Viet Approval of the Chairman of the Master’s Thesis Defense Council and the Dean of faculty of Civil Engineering after the thesis being corrected.
CHAIRMAN OF THE COUNCIL DEAN OF FACULTY OF CIVIL ENGINEERING i VIETNAM NATIONAL UNIVERSITY SOCIALIST REPUBLIC OF VIETNAM HO CHI MINH CITY Independence – Freedom - Happiness HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY THE TASK SHEET OF MASTER’S THESIS Full name: LÊ VĂN TRỌNG Student code: 2192017 Date of birth: 26/01/1992 Place of birth: HCM city Major : Construction Management Major code : 8580302 I. THESIS TOPIC: Energy and Resource Optimization in Building Smart City Using Hybrid-Multiver Optimizer (MOV) Algorithm. Tối Ưu Năng Lượng Và Tài Nguyên Trong Xây Dựng Thành Phố Thông Minh Sử Dụng Thuật Toán Lai Đa Vũ Trụ (MVO). TASKS AND CONTENTS: Energy optimisation artificial intelligence (Ai) in construction management.
TASKS STARTING DATE : October 2022. TASKS ENDING DATE : August 2023. INSTRUCTOR : Associate Professor Pham Vu Hong Son HCM City, August 2023 INSTRUCTOR HEAD OF DEPARTMENT Pham Vu Hong Son DEAN OF FACULTY OF CIVIL ENGINEERING ii ACKNOWLEDGEMENT First and foremost, I would like to show my appreciation to my enthusiastic thesis instructor and advisor, Ph. Pham Vu Hong Son, for his invaluable guidance and unwavering support throughout this research journey.
His expertise, constructive feedback, and insightful suggestions have played a pivotal role in guiding and give me direction for my thesis from begging to the end. I am extremely appreciative for his devotion, persistence, and willingness to share his knowledge, which have greatly enriched my understanding of the subject matter. I am fortunate to have had the opportunity to work under his mentorship, and I extend my heartfelt thanks for his valuable contributions to this research. iii ABSTRACT The increasing demand for clean and efficient energy for construction industry, especially for developing and managing smart cities has led to the development of microgrids.
Common problems with these strategies are the demand and supply of the energy constantly conflicted, as a result, the energy usage frequently inefficient. To solve this problem, optimization techniques and heuristics methods are utilized. Mathematical optimization procedures can acquire optimum results, but they are only suitable for small-scale problems. For large-scale situation, artificial intelligence techniques have been applied.
In this thesis, a Hybrid version of the multi-verse optimizer (MVO) and the Sine Cosine Algorithm (SCA) is introduced to advance the exploration and exploitation balance of the standard MVO algorithm. The proposed hybrid algorithms also find improved optimal solutions for energy optimization by illustrating its searching ability with diverse search space problems. As a result, the proposed algorithm will demonstrate its availability to solve real unknown search space construction and non-construction problems. Keywords: Energy management, Hybrid multi-verse optimizer (MVO), Artificial intelligent, Smart city.
iv TÓM TẮT LUẬN VĂN THẠC SĨ Nhu cầu ngày càng tăng về năng lượng sạch và hiệu quả trong ngành xây dựng, đặc biệt là cho việc phát triển và quản lý các thành phố thông minh, đã dẫn đến việc phát triển các mạng lưới nhỏ. Vấn đề phổ biến với các chiến lược này là sự xung đột giữa nhu cầu và cung cấp năng lượng, dẫn đến việc sử dụng năng lượng thường không hiệu quả. Để giải quyết vấn đề này, các kỹ thuật tối ưu hóa và phương pháp thông minh được áp dụng. Các quy trình tối ưu hóa toán học có thể đạt được kết quả tối ưu, nhưng chúng chỉ phù hợp với các vấn đề quy mô nhỏ.
Đối với các tình huống quy mô lớn, các kỹ thuật trí tuệ nhân tạo đã được áp dụng. Trong luận văn này, một phiên bản Hybrid của thuật toán tối ưu hỗn hợp multi-verse (MVO) và thuật toán Sine Cosine (SCA) được giới thiệu để cải thiện sự cân bằng giữa việc khám phá và khai thác của thuật toán MVO tiêu chuẩn. Các thuật toán hybrid được đề xuất cũng tìm kiếm các giải pháp tối ưu cải thiện cho việc tối ưu hóa năng lượng bằng cách minh họa khả năng tìm kiếm của nó với các vấn đề không gian tìm kiếm đa dạng. Kết quả là, thuật toán đề xuất sẽ chứng minh tính khả thi của nó trong việc giải quyết các vấn đề xây dựng và không xây dựng trong không gian tìm kiếm thực sự.
Từ khóa: Quản lý năng lượng, Tối ưu hỗn hợp multi-verse (MVO), Trí tuệ nhân tạo, Thành phố thông minh. v AUTHOR’S COMMITMENT The undersigned below: Name : Le Van Trong Student ID : 2192017 Place and date of born : Ho Chi Minh City, 26th January 1992. Address : 687 Lac Long Quan, Ward 10, Tan Binh District, Ho Chi Minh City. With this declaring that the master thesis entitled “Energy And Resource Optimization in Building Smart City Using Multiverse Optimizer (MOV) Algorithm” is done by the author under supervision of the instructor.
All works, ideas, and material that was gain from other references have been cited in the corrected way. Ho Chi Minh City, August 06 2023 Le Van Trong vi TABLE OF CONTENTS THE TASK SHEET OF MASTER’S THESIS. iii TÓM TẮT LUẬN VĂN THẠC SĨ. iv AUTHOR’S COMMITMENT.
v TABLE OF CONTENTS. vi LIST OF FIRGURES. Scope of study. Academic and Practical Significances.
Definition of Smart City. Energy/Resource optimization in Construction. Energy Optimization in Smart city construction. Sine Cosine Algorithm (SCA).
Hybrid Multiverse – Sincos Algorithm (hMVO) for Smart city construction energy cost effective optimization. Cost effective optimization. Hybrid Multiverse – Sincos Algorithm (hMVO). Hybrid Multiverse – Sincos Algorithm (hMVO) for Smart city construction energy cost effective optimization.
CONCLUSION AND RECOMMENDATION. Demonstration Projects, networking and education:. Government Incentives and Policies. Research and Development.
79 Run_mvo_sca. 87 viii LIST OF FIRGURES Figure 1-1. Energy Optimization in smart city project. Scope of study.
Conceptual model of the proposed MVO algorithm. Wormhole existence probability (WEP) versus travelling distance rate (TDR). Flow chart of MVO. Effects of Sine and Cosine regarding equation (12) and equation (13) on the next position.
Sine and cosine with range of [−2,2]. Sine and cosine with the range in [−2,2] allow a solution to go around (inside the space between them) or beyond (outside the space between them) the destination. 39 Figure 3-7: The model gradually reduces the range of the Sine and Cosine functions. Flow chart of MVO.
Flowchart of Hybrid Multiverse–Sincos Algorithm (hMVO) algorithm. Wind plants, PV plants, and CHP as DERs (Distributed Energy Resources). Convergence graph at Hour 17 for Case 1. 51 x LIST OF TABLES Table 2-1.
List of related studies. Required power for each hour of case 1 [19]. The power generation of each renewable energy source per hour [19]. Cost coefficients of DERs in microgrid in case 1 [19].
Generation power schedule and its cost generate by CMVO. Generation power schedule and its cost generate by hMVO. Statistic results for each algorithm performance Case 1. Required power for each hour of case 2.
The power generation of each renewable energy source per hour Case 2. Generation power schedule and its cost generate by CMVO. Generation power schedule and its cost generate by hMVO. Statistic results for each algorithm performance Case 2.54 xi LIST OF ABBREVIATIONS AEC Architecture, engineering, and construction AHA Artificial hummingbird algorithm AI Artificial Intelligence ANN Artificial neural network BREEAM Building Research Establishment Environmental Assessment Method CEM Construction engineering and management CHP Combine heat and power plant DE Differential evolution DERs Distributed energy resources ES Evolution strategy GA Genetic Algorithms hMVO Hybrid Multi-Verse Optimization Algorithm LCA Life Cycle Assessment LEED Leadership in Energy and Environmental Design MVO Hybrid Multi-Verse Optimization Algorithm OF Objective Function PSO Particle Swarm Optimization PV Solar power plant SCA Sine Cosine Algorithm TDR Travelling distance rate WEP Wormhole existence probability WP Wind plant 1 1.
INTRODUCTION § In this chapter, the research problem is introduced, emphasizing the significance of optimizing power schedules to minimize generation costs.1 provides a concise overview of power schedule optimization, while Section 1.2 outlines the research objectives. The scope of the study is presented in Section 1.3, after that an explanation of the research methodology is discussed in Section 1. The academic and practical significances of the research are addressed in Section 1.5, concluding this chapter. Research Problem Smart cities utilize advanced technology and innovative solutions to address urban challenges, leading to enhanced quality of life, prosperity, and sustainability.
As a result, smart cities are better equipped to handle challenges compared to conventional cities. Global Data’s review of smart city history traces the first smart city back to Amsterdam, which established a virtual digital city in 1994. IBM's "Smarter Cities" marketing initiative launched in 2008, and the Smart City Expo World Congress commenced in Barcelona in 2011, becoming an annual event charting smart city development worldwide. The European Commission also created the Smart Cities Marketplace in 2012 to centralize urban initiatives within the European Union.
Presently, more than 165 cities from 80 countries are participating in smart city projects in various capacities. A smart city encompasses a wide array of elements, necessitating a strategic and systematic approach for effective implementation. The strategic framework of a smart city comprises a hierarchical system encompassing vision, core values, and strategic goals. The vision outlines the future smart cities aim to achieve, and core values and strategic goals are derived from this vision.
However, smart city development varies among cities, leading to differing goals and evaluation criteria in project execution. 2 Reviewing previous smart city studies reveals trends and weaknesses in strategies. While research on smart cities has increased significantly, many studies only focus on the application of smart technologies like Big Data and ICT or present case-specific anecdotes, lacking a consistent and systematic strategic approach. To ensure efficient resource allocation and utilization, smart city development should prioritize selection and concentration.
However, current research tends to be fragmented and technology-focused, lacking a comprehensive framework for setting effective strategic goals. Energy resources optimization is a key component of sustainable construction practices in smart city projects. Construction managers play a vital role in planning, designing, and executing construction projects with a focus on reducing energy consumption, optimizing energy use, and integrating renewable energy sources. Energy-Efficient Building Design which stage construction managers are involved in making decisions related to building design and material selection.
By considering energy-efficient building design principles and technologies, they can optimize energy use and reduce operational costs throughout the building's lifecycle. Construction Equipment and Energy Management is where managers can contribute to energy resources optimization by efficiently managing construction equipment and machinery. They can schedule equipment usage to avoid energy wastage and explore the use of energy-efficient machinery. Construction managers also can conduct life cycle cost analysis to evaluate the long-term costs and benefits of different energy resource optimization strategies.
This analysis helps in making informed decisions about energy-efficient technologies and practices. Construction engineering and management (CEM) is a specialized field that utilizes project management principles to supervise the entire lifecycle of construction projects, encompassing tasks such as planning, design, construction, and maintenance.