Mở bài
Chủ đề về công nghệ thông minh và hiệu quả năng lượng tòa nhà (Smart Technology’s Role In Building Energy Efficiency) đang ngày càng trở nên phổ biến trong các đề thi IELTS Reading gần đây. Đây là một trong những chủ đề thuộc nhóm Technology, Environment và Urban Development – những lĩnh vực thường xuyên xuất hiện trong kỳ thi IELTS Academic. Với xu hướng phát triển bền vững và thành phố thông minh trên toàn cầu, việc nắm vững kiến thức và từ vựng liên quan đến chủ đề này sẽ giúp bạn tự tin hơn khi đối mặt với đề thi thực tế.
Trong bài viết này, bạn sẽ được thực hành với một bộ đề thi IELTS Reading hoàn chỉnh bao gồm 3 passages với độ khó tăng dần từ Easy đến Hard, đúng như format thi thật. Bạn sẽ làm quen với 40 câu hỏi thuộc nhiều dạng khác nhau như Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác. Đặc biệt, mỗi câu hỏi đều có đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, cách paraphrase và kỹ thuật làm bài hiệu quả. Phần từ vựng quan trọng được tổng hợp theo từng passage sẽ giúp bạn mở rộng vốn từ học thuật một cách có hệ thống.
Đề thi này phù hợp cho học viên có trình độ từ band 5.0 trở lên và mong muốn cải thiện kỹ năng Reading để đạt band điểm mục tiêu.
1. Hướng dẫn làm bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test kéo dài trong 60 phút và bao gồm 3 passages với tổng cộng 40 câu hỏi. Điểm đặc biệt là bạn không có thời gian thêm để chuyển đáp án sang phiếu trả lời, vì vậy quản lý thời gian là yếu tố then chốt để đạt điểm cao.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (độ khó Easy, 13 câu hỏi)
- Passage 2: 18-20 phút (độ khó Medium, 13 câu hỏi)
- Passage 3: 23-25 phút (độ khó Hard, 14 câu hỏi)
Mỗi passage sẽ có độ dài từ 650-1000 từ và độ khó tăng dần. Đừng dành quá nhiều thời gian cho Passage 1, hãy để dành sức lực cho Passage 3 – nơi thường chứa những câu hỏi phức tạp và đòi hỏi kỹ năng phân tích cao.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm đầy đủ các dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Chọn đáp án đúng từ các lựa chọn A, B, C, D
- True/False/Not Given – Xác định thông tin đúng, sai hay không được đề cập
- Matching Headings – Nối tiêu đề phù hợp với mỗi đoạn văn
- Sentence Completion – Hoàn thành câu với từ trong bài đọc
- Summary Completion – Điền từ vào đoạn tóm tắt
- Matching Features – Nối thông tin với các đặc điểm tương ứng
- Short-answer Questions – Trả lời câu hỏi ngắn dựa trên thông tin trong bài
Mỗi dạng câu hỏi yêu cầu kỹ năng đọc hiểu khác nhau, từ skimming, scanning đến đọc chi tiết và suy luận logic.
2. IELTS Reading Practice Test
PASSAGE 1 – The Evolution of Smart Buildings
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
The concept of smart buildings has transformed dramatically over the past few decades, evolving from simple automated systems to sophisticated networks that can optimize energy consumption and enhance occupant comfort. Today, these intelligent structures represent a crucial solution to the growing concerns about energy efficiency and environmental sustainability in urban areas.
Early developments in building automation began in the 1980s when engineers first introduced basic computer systems to control heating, ventilation, and air conditioning (HVAC). These rudimentary systems could adjust temperatures based on preset schedules, but they lacked the ability to respond to real-time conditions or learn from patterns. The technology was expensive and primarily limited to large commercial buildings where the initial investment could be justified by the potential energy savings.
The introduction of sensor technology in the 1990s marked a significant turning point. Buildings could now detect occupancy, measure temperature variations, and monitor light levels throughout different areas. This data allowed automated systems to make more informed decisions about when and where to allocate resources. For instance, if sensors detected that a conference room was empty, the system could automatically reduce heating or cooling and dim the lights, thereby conserving energy without compromising comfort when the room was in use.
Internet connectivity revolutionized smart building technology in the early 2000s. The ability to connect various building systems through a centralized network meant that facility managers could monitor and control operations from anywhere. More importantly, these connected systems could share information with each other, creating a more cohesive approach to building management. A security system detecting movement after hours could signal the lighting system to illuminate specific areas, while simultaneously notifying the HVAC system to adjust temperatures only in occupied zones.
The current generation of smart buildings incorporates artificial intelligence and machine learning algorithms that can predict and adapt to usage patterns. These systems analyze historical data to understand when certain areas of a building are likely to be occupied and can adjust settings proactively rather than reactively. For example, if data shows that a particular office floor is typically busy on Monday mornings, the system can begin warming the space and increasing ventilation thirty minutes before employees arrive, ensuring optimal conditions while minimizing wasted energy during unoccupied periods.
Integration with renewable energy sources has become another hallmark of modern smart buildings. Solar panels, wind turbines, and other alternative energy systems can now communicate with building management systems to optimize when and how energy is used. On sunny days, a smart building might prioritize running energy-intensive equipment like washing machines or charging electric vehicles when solar power generation is at its peak. Conversely, during periods of low renewable energy production, the system can reduce non-essential energy consumption or draw power from stored battery reserves.
The environmental impact of smart buildings extends beyond simple energy conservation. By optimizing resource use, these structures significantly reduce carbon emissions and help cities meet their sustainability targets. A typical smart building can reduce energy consumption by 30-50% compared to traditional buildings of similar size and function. When multiplied across thousands of buildings in a major city, these savings translate into substantial reductions in greenhouse gas emissions and decreased strain on electrical grids during peak demand periods.
However, the implementation of smart building technology is not without challenges. The initial capital investment required to retrofit existing buildings with sensors, advanced controls, and integrated systems can be substantial, often requiring several years to recoup through energy savings. There are also concerns about data privacy and cybersecurity, as connected buildings collect vast amounts of information about occupant behavior and building operations. Ensuring this data is protected from unauthorized access while still allowing the system to function effectively requires careful planning and robust security measures.
Looking ahead, experts predict that future smart buildings will become even more responsive and efficient. Emerging technologies like digital twins – virtual replicas of physical buildings that can simulate different scenarios – allow engineers to test optimization strategies before implementing them in real structures. The integration of smart buildings with smart city infrastructure will create opportunities for even greater efficiency, such as coordinating building energy consumption with citywide power generation and distribution systems.
Questions 1-13
Questions 1-6: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. What was the main limitation of early building automation systems in the 1980s?
- A. They were too expensive for most buildings
- B. They could not respond to changing conditions in real-time
- C. They only worked in residential buildings
- D. They consumed more energy than they saved
2. According to the passage, sensor technology in the 1990s enabled buildings to:
- A. Generate their own electricity
- B. Communicate with other buildings
- C. Detect presence and environmental conditions
- D. Reduce construction costs
3. The introduction of internet connectivity in the early 2000s was significant because it:
- A. Reduced the cost of building construction
- B. Allowed different building systems to share information
- C. Eliminated the need for facility managers
- D. Made buildings completely automatic
4. Modern smart buildings with AI can:
- A. Replace all human workers
- B. Generate more energy than they consume
- C. Predict occupancy patterns and adjust settings in advance
- D. Operate without any internet connection
5. What is mentioned as a challenge for implementing smart building technology?
- A. Lack of trained personnel
- B. Insufficient energy savings
- C. High initial investment costs
- D. Unavailability of necessary equipment
6. Digital twins are described as:
- A. Backup systems for when primary systems fail
- B. Virtual models used for testing optimization strategies
- C. Physical copies of buildings in different locations
- D. Alternative energy generation systems
Questions 7-10: True/False/Not Given
Do the following statements agree with the information given in the passage?
Write:
- TRUE if the statement agrees with the information
- FALSE if the statement contradicts the information
- NOT GIVEN if there is no information on this
7. Basic building automation systems in the 1980s were only installed in residential homes.
8. Smart buildings can reduce energy consumption by 30-50% compared to traditional buildings.
9. All existing buildings have been successfully converted to smart buildings.
10. Data privacy is a concern associated with smart building technology.
Questions 11-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
11. In the 1990s, automated systems could make better decisions about when to __ based on sensor data.
12. Smart buildings can coordinate the use of energy-intensive equipment with __ from solar panels.
13. Future smart buildings will integrate with __ to achieve greater efficiency across entire urban areas.
PASSAGE 2 – Smart Technologies Transforming Building Energy Management
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The integration of smart technologies into building energy management systems represents a paradigm shift in how we approach resource conservation and operational efficiency. Unlike conventional buildings that operate on fixed schedules and manual adjustments, smart buildings employ an array of interconnected devices and sophisticated algorithms to create dynamic, responsive environments that adapt to changing conditions in real-time. This transformation is not merely about installing new equipment; it fundamentally reconceptualizes the relationship between buildings, their occupants, and the broader energy ecosystem.
A. The Internet of Things Infrastructure
At the heart of smart building technology lies the Internet of Things (IoT) – a network of physical devices embedded with sensors, software, and connectivity capabilities that enable them to collect and exchange data. In a typical smart building, thousands of IoT sensors monitor diverse parameters including temperature, humidity, air quality, occupancy, light levels, and energy consumption across different zones. These sensors generate massive streams of data that feed into centralized analytics platforms, where machine learning algorithms identify patterns, detect anomalies, and recommend optimization strategies. The granularity of this data collection allows facility managers to understand precisely how energy is being used throughout a building and identify specific areas where efficiency improvements can be achieved.
B. Advanced HVAC Optimization
Heating, ventilation, and air conditioning systems typically account for 40-60% of a building’s total energy consumption, making them the primary target for smart optimization efforts. Traditional HVAC systems operate on predetermined schedules or simple thermostatic controls, often heating or cooling spaces regardless of actual occupancy. In contrast, smart HVAC systems utilize predictive algorithms that analyze historical usage patterns, weather forecasts, and real-time occupancy data to optimize temperature control. These systems can precondition spaces before occupants arrive, adjust airflow based on detected CO2 levels, and create microclimatic zones that provide personalized comfort while minimizing energy waste. Some advanced systems even employ thermal energy storage, cooling water or other materials during off-peak hours when electricity is cheaper and renewable energy is more abundant, then using this stored cooling capacity during peak demand periods.
C. Intelligent Lighting Systems
Lighting represents another significant component of building energy consumption, particularly in commercial settings. Smart lighting systems go far beyond simple motion sensors by incorporating daylight harvesting technologies that adjust artificial lighting based on available natural light. Occupancy-sensing fixtures can dim or brighten lights based on the number of people in a space and their activities. More sophisticated systems use circadian lighting principles, adjusting both the intensity and color temperature of light throughout the day to support occupants’ natural biological rhythms, which research suggests can enhance productivity and well-being while also reducing energy consumption. The integration of LED technology with smart controls has made these systems increasingly cost-effective, with some installations achieving payback periods of less than three years through energy savings alone.
Hệ thống quản lý năng lượng tòa nhà thông minh với cảm biến IoT và điều khiển tự động
D. Energy Storage and Grid Integration
The proliferation of renewable energy sources has introduced new complexities and opportunities for building energy management. Solar and wind power generation is inherently variable, producing energy when weather conditions are favorable rather than when demand is highest. Smart buildings address this challenge through sophisticated energy storage systems and intelligent grid integration. Battery storage systems can capture excess renewable energy during periods of high generation and release it when needed, reducing reliance on grid electricity during peak hours when power is most expensive and carbon-intensive. Furthermore, advanced buildings can participate in demand response programs, automatically reducing energy consumption during grid stress events in exchange for financial incentives. This bidirectional relationship between buildings and the electrical grid transforms structures from passive consumers into active participants in energy ecosystem management.
E. Predictive Maintenance and Asset Management
Beyond immediate energy savings, smart building systems provide substantial long-term value through predictive maintenance capabilities. By continuously monitoring equipment performance and analyzing trends in operational data, these systems can identify potential failures before they occur, allowing maintenance to be scheduled proactively rather than reactively. This approach not only prevents costly emergency repairs and downtime but also ensures that equipment operates at peak efficiency throughout its lifecycle. For example, a gradual increase in energy consumption by a particular HVAC unit might indicate developing inefficiencies due to dirty filters, refrigerant leaks, or mechanical wear – issues that, if addressed early, can prevent both equipment failure and prolonged periods of excessive energy use.
F. Occupant Engagement and Behavioral Change
While technology provides the tools for energy optimization, human behavior remains a critical factor in achieving sustainability goals. Smart building systems increasingly incorporate features designed to engage occupants and encourage energy-conscious behavior. Real-time feedback displays showing energy consumption can make abstract concepts tangible, helping people understand the impact of their actions. Some systems gamify energy conservation, creating competitions between floors or departments to encourage reduced consumption. Mobile applications allow occupants to customize their local environment within predetermined efficiency parameters, providing a sense of control while maintaining overall system optimization. Research indicates that buildings incorporating such occupant engagement strategies achieve 10-20% greater energy savings than those relying solely on automated controls.
G. Data Analytics and Continuous Improvement
The true power of smart building technology lies not in any single component but in the continuous improvement cycle enabled by comprehensive data analytics. As systems collect more data over time, their algorithms become increasingly accurate at predicting patterns and identifying optimization opportunities. Performance benchmarking against similar buildings reveals whether a structure is operating as efficiently as possible or if additional improvements are warranted. This analytical approach transforms building management from an operational necessity into a strategic function that contributes directly to organizational sustainability objectives and financial performance.
Questions 14-26
Questions 14-19: Matching Headings
The passage has seven sections, A-G. Choose the correct heading for each section from the list of headings below.
List of Headings:
i. The role of human behavior in energy conservation
ii. Network technology enabling smart building systems
iii. Temperature control systems and energy reduction
iv. Storage solutions for renewable energy challenges
v. Lighting control systems adapting to natural conditions
vi. Long-term equipment monitoring and efficiency
vii. Data analysis for ongoing performance enhancement
viii. Cost barriers to implementing smart technology
ix. Government regulations for building efficiency
14. Section A
15. Section B
16. Section C
17. Section D
18. Section E
19. Section F
Questions 20-23: Yes/No/Not Given
Do the following statements agree with the claims of the writer in the passage?
Write:
- YES if the statement agrees with the claims of the writer
- NO if the statement contradicts the claims of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
20. Smart buildings completely eliminate the need for human decision-making in energy management.
21. HVAC systems are the largest consumers of energy in most buildings.
22. Circadian lighting systems can potentially improve occupant productivity.
23. All smart building systems pay for themselves within one year through energy savings.
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Smart buildings use IoT sensors to collect detailed information about building operations. The data is processed by 24. __ that identify patterns and suggest improvements. Unlike traditional systems, smart HVAC can create 25. __ that provide customized comfort for different areas. Buildings can also participate in 26. __, reducing consumption during times of high grid demand in return for payment.
PASSAGE 3 – The Multifaceted Impact of Smart Building Technologies on Urban Energy Systems
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The proliferation of smart building technologies represents a convergence of multiple technological trajectories – artificial intelligence, sensor networks, renewable energy systems, and data analytics – that collectively promise to fundamentally reshape urban energy landscapes. While the immediate benefits of enhanced energy efficiency and reduced operational costs have driven initial adoption, the broader implications of widespread smart building deployment extend far beyond individual structures to encompass systemic transformations in how cities generate, distribute, and consume energy. Understanding these multidimensional impacts requires examining not only the technical capabilities of smart systems but also their interactions with regulatory frameworks, economic incentives, social dynamics, and environmental objectives.
The technical architecture of contemporary smart buildings relies on hierarchical control systems that operate across multiple temporal and spatial scales. At the most granular level, localized controllers manage individual components such as variable-speed fans, dimmable lights, or motorized window shades, responding to immediate sensory inputs with millisecond-level latency. These local controllers communicate with zone-level supervisory systems that coordinate activities across related equipment to optimize comfort and efficiency for specific building areas, typically operating on timescales of seconds to minutes. Zone systems, in turn, report to building-level management platforms that synthesize information across the entire structure, identify optimization opportunities that transcend individual zones, and coordinate major operational decisions on timescales of hours to days. Finally, the most sophisticated implementations feature portfolio-level analytics that aggregate data across multiple buildings, enabling comparative performance analysis, identification of best practices, and strategic resource allocation across an organization’s entire real estate holdings.
This nested control architecture generates substantial complexity, particularly regarding the optimization algorithms that govern system behavior. Early smart building systems relied primarily on rule-based approaches, where engineers explicitly programmed specific responses to defined conditions: if occupancy is detected and temperature drops below a threshold, activate heating. While transparent and predictable, such approaches struggle to address the multivariable optimization problems inherent in building management, where decisions affecting temperature, air quality, lighting, and energy costs must be simultaneously balanced against competing objectives. Contemporary systems increasingly employ machine learning techniques that can discover complex, non-linear relationships within building data and develop control strategies that would be difficult or impossible to explicitly program.
However, the application of machine learning to building management introduces its own challenges, particularly regarding algorithmic transparency and predictive reliability. Neural networks and other black-box algorithms may achieve superior performance but provide limited insight into why specific decisions are made, complicating troubleshooting when systems behave unexpectedly. Moreover, machine learning models trained on historical data may perform poorly when confronted with novel conditions outside their training distribution, such as extreme weather events or unusual occupancy patterns resulting from unexpected circumstances. Balancing the performance advantages of sophisticated algorithms against the need for operational reliability and human oversight remains an active area of research and practical concern.
Công nghệ học máy và trí tuệ nhân tạo tối ưu hóa năng lượng tòa nhà hiện đại
The economic dimensions of smart building adoption involve complex considerations beyond simple calculations of energy savings versus implementation costs. While energy efficiency improvements typically drive the initial business case, substantial additional value derives from enhanced asset performance, improved occupant satisfaction, and risk mitigation. Studies have documented that buildings with superior environmental conditions experience lower employee turnover, reduced absenteeism, and higher productivity, though quantifying these benefits and attributing them specifically to smart building features presents methodological challenges. The predictive maintenance capabilities of smart systems reduce costly equipment failures and extend asset lifespans, though these benefits accrue over extended timeframes that complicate return-on-investment calculations, particularly for organizations with shorter investment horizons or higher discount rates.
From a macroeconomic perspective, the widespread adoption of smart building technologies has catalyzed innovation across multiple related industries. The demand for specialized sensors has spurred development of increasingly sophisticated, cost-effective detection technologies with applications extending beyond buildings into healthcare, manufacturing, and transportation. The data analytics platforms developed for building management have been adapted for diverse applications including predictive maintenance in industrial settings and optimization of agricultural operations. This technology spillover generates economic value that extends well beyond the building sector itself, though remains challenging to quantify comprehensively.
The interaction between smart buildings and electrical grid operations represents perhaps the most significant systemic impact of these technologies. Traditional electrical grids operate as fundamentally unidirectional systems, with centralized power plants generating electricity that flows through transmission and distribution networks to passive consumers. This architecture requires grid operators to continuously balance generation and consumption in real-time, maintaining system frequency within narrow tolerances. The inflexibility of traditional buildings, which consume power according to occupant demands with little regard for grid conditions, necessitates maintaining substantial reserve generation capacity to meet peak demands that may occur only a few hours per year, representing enormous economic and environmental costs.
Smart buildings possess the potential to fundamentally alter this relationship through several mechanisms. Demand flexibility – the ability to shift electricity consumption to different times without compromising occupant comfort – allows buildings to reduce consumption during peak periods or increase it when renewable generation is abundant. A smart building might precool its thermal mass during morning hours when solar generation is increasing, allowing it to reduce cooling system operation during afternoon peak demand periods while maintaining comfortable temperatures. At scale, coordinated demand flexibility across thousands of buildings can provide grid services comparable to conventional power plants, potentially deferring or eliminating the need for expensive infrastructure upgrades while facilitating higher penetrations of variable renewable energy.
However, realizing this potential requires addressing substantial institutional and regulatory barriers. Most electricity rate structures were designed for a paradigm of passive consumption, providing limited financial incentives for demand flexibility. Transactive energy frameworks that enable buildings to respond dynamically to real-time price signals or direct grid service requests remain in early stages of development and deployment. Concerns about cybersecurity vulnerabilities associated with grid-connected building systems have prompted legitimate questions about the resilience of energy infrastructure increasingly dependent on digital communications. The governance questions surrounding who controls building energy systems – building owners, tenants, grid operators, or some combination – and how competing interests are reconciled remain contentious and unresolved in many jurisdictions.
The environmental implications of smart building technologies extend beyond direct energy savings to encompass broader questions of sustainable urban development and climate change mitigation. Buildings account for approximately 40% of global energy consumption and one-third of greenhouse gas emissions, making building efficiency crucial to achieving international climate objectives. However, environmental assessments must consider the embodied energy and environmental impacts associated with manufacturing, installing, and eventually disposing of smart building technologies themselves. Life-cycle analyses suggest that for most applications, the operational energy savings substantially outweigh embodied impacts within a few years, but results depend heavily on specific technologies, installation contexts, and assumed system lifespans. Furthermore, the rebound effect – wherein efficiency improvements enable increased consumption rather than absolute reductions – presents a perennial concern that requires monitoring and potentially policy interventions to address.
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, contemporary smart buildings operate using:
- A. A single centralized control system
- B. Multiple control levels operating at different timescales
- C. Completely autonomous equipment with no coordination
- D. Manual controls adjusted by facility managers
28. What is mentioned as a limitation of rule-based control systems?
- A. They are too expensive to implement
- B. They cannot handle multiple competing objectives simultaneously
- C. They respond too slowly to changing conditions
- D. They require constant manual programming
29. The main concern with machine learning algorithms in building management is:
- A. They consume too much computational power
- B. They are too expensive to develop
- C. They lack transparency and may perform poorly in unusual conditions
- D. They cannot achieve better performance than traditional methods
30. According to the passage, the economic value of smart buildings includes all of the following EXCEPT:
- A. Reduced energy costs
- B. Improved employee productivity
- C. Extended equipment lifespan
- D. Decreased building construction time
31. What does the passage suggest about traditional electrical grids?
- A. They are more efficient than modern smart grids
- B. They require maintaining excess capacity for peak demand periods
- C. They cannot distribute power to buildings effectively
- D. They have been completely replaced by new systems
Questions 32-36: Matching Features
Match each description (32-36) with the correct concept (A-H) from the list below.
A. Neural networks
B. Demand flexibility
C. Embodied energy
D. Transactive energy frameworks
E. Rebound effect
F. Hierarchical control systems
G. Thermal mass
H. Portfolio-level analytics
32. Systems that enable buildings to adjust electricity usage based on real-time grid conditions
33. The phenomenon where efficiency gains lead to increased overall consumption
34. Algorithms that achieve good results but provide little explanation for their decisions
35. Environmental impacts from manufacturing and installing smart technologies
36. Comparative analysis across multiple buildings to identify optimization opportunities
Questions 37-40: Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What type of capacity do grid operators need to maintain to meet occasional high demand?
38. What type of vulnerabilities are mentioned as a concern for grid-connected building systems?
39. What percentage of global energy consumption is attributed to buildings?
40. What term describes the energy and impacts associated with producing smart building equipment?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- B
- C
- C
- B
- FALSE
- TRUE
- NOT GIVEN
- TRUE
- allocate resources
- peak/power generation
- smart city infrastructure
PASSAGE 2: Questions 14-26
- ii
- iii
- v
- iv
- vi
- i
- NO
- YES
- YES
- NOT GIVEN
- machine learning algorithms
- microclimatic zones
- demand response programs
PASSAGE 3: Questions 27-40
- B
- B
- C
- D
- B
- D
- E
- A
- C
- H
- reserve generation capacity
- cybersecurity vulnerabilities
- 40% / forty percent
- embodied energy
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: early building automation systems, 1980s, limitation
- Vị trí trong bài: Đoạn 2, dòng 1-4
- Giải thích: Bài đọc nói rõ “These rudimentary systems could adjust temperatures based on preset schedules, but they lacked the ability to respond to real-time conditions or learn from patterns.” Đáp án B (They could not respond to changing conditions in real-time) là paraphrase chính xác của câu này. Đáp án A đúng một phần nhưng không phải là “main limitation” được nhấn mạnh trong bài.
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: sensor technology, 1990s, enabled
- Vị trí trong bài: Đoạn 3, dòng 1-3
- Giải thích: “Buildings could now detect occupancy, measure temperature variations, and monitor light levels” – Đây chính là khả năng detect presence and environmental conditions (đáp án C). Bài không đề cập đến việc phát điện (A), giao tiếp với tòa nhà khác (B), hay giảm chi phí xây dựng (D).
Câu 3: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: internet connectivity, early 2000s, significant
- Vị trí trong bài: Đoạn 4, dòng 2-5
- Giải thích: “More importantly, these connected systems could share information with each other” – đây là lý do quan trọng được nêu trong bài. Đáp án B (Allowed different building systems to share information) paraphrase chính xác ý này.
Câu 4: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: modern smart buildings, AI, can
- Vị trí trong bài: Đoạn 5, dòng 1-5
- Giải thích: “These systems analyze historical data to understand when certain areas of a building are likely to be occupied and can adjust settings proactively rather than reactively” – Đáp án C (Predict occupancy patterns and adjust settings in advance) tóm tắt chính xác khả năng này.
Câu 5: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: challenge, implementing
- Vị trí trong bài: Đoạn 8, dòng 1-3
- Giải thích: “The initial capital investment required to retrofit existing buildings… can be substantial” – Đáp án C (High initial investment costs) được nêu rõ là một thách thức trong việc triển khai công nghệ.
Câu 6: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: digital twins
- Vị trí trong bài: Đoạn 9, dòng 2-4
- Giải thích: “digital twins – virtual replicas of physical buildings that can simulate different scenarios – allow engineers to test optimization strategies” – Đáp án B (Virtual models used for testing optimization strategies) là paraphrase chính xác.
Câu 7: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: 1980s, only residential homes
- Vị trí trong bài: Đoạn 2, dòng 4-6
- Giải thích: Bài nói “primarily limited to large commercial buildings” – mâu thuẫn trực tiếp với “only residential homes”, do đó đáp án là FALSE.
Câu 8: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: reduce energy consumption, 30-50%
- Vị trí trong bài: Đoạn 7, dòng 3-4
- Giải thích: “A typical smart building can reduce energy consumption by 30-50% compared to traditional buildings” – Thông tin khớp hoàn toàn với câu hỏi.
Câu 9: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: all existing buildings, successfully converted
- Vị trí trong bài: Không có thông tin
- Giải thích: Bài chỉ nói về những thách thức trong việc retrofit existing buildings nhưng không đề cập đến việc tất cả đã được chuyển đổi thành công hay chưa.
Câu 10: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: data privacy, concern
- Vị trí trong bài: Đoạn 8, dòng 4-5
- Giải thích: “There are also concerns about data privacy and cybersecurity” – Câu này khẳng định rõ ràng data privacy là một concern.
Câu 11: allocate resources
- Dạng câu hỏi: Sentence Completion
- Từ khóa: 1990s, automated systems, better decisions
- Vị trí trong bài: Đoạn 3, dòng 3-5
- Giải thích: “This data allowed automated systems to make more informed decisions about when and where to allocate resources” – Cụm “allocate resources” xuất hiện nguyên văn.
Câu 12: peak/power generation
- Dạng câu hỏi: Sentence Completion
- Từ khóa: energy-intensive equipment, solar panels
- Vị trí trong bài: Đoạn 6, dòng 4-6
- Giải thích: “a smart building might prioritize running energy-intensive equipment… when solar power generation is at its peak” – Có thể dùng “peak” hoặc “power generation”.
Câu 13: smart city infrastructure
- Dạng câu hỏi: Sentence Completion
- Từ khóa: future smart buildings, integrate, greater efficiency
- Vị trí trong bài: Đoạn 9, dòng 4-6
- Giải thích: “The integration of smart buildings with smart city infrastructure will create opportunities for even greater efficiency” – Cụm “smart city infrastructure” là đáp án chính xác.
Giải thích chi tiết đáp án IELTS Reading với vị trí thông tin trong bài đọc
Passage 2 – Giải Thích
Câu 14: ii (Section A – The Internet of Things Infrastructure)
- Dạng câu hỏi: Matching Headings
- Giải thích: Section A tập trung vào “Internet of Things (IoT) – a network of physical devices” và cách các sensors và connectivity tạo nên infrastructure cho smart buildings. Heading ii “Network technology enabling smart building systems” phù hợp nhất.
Câu 15: iii (Section B – Advanced HVAC Optimization)
- Dạng câu hỏi: Matching Headings
- Giải thích: Toàn bộ section B nói về “smart HVAC systems” và cách chúng optimize temperature control để giảm energy consumption. Heading iii “Temperature control systems and energy reduction” mô tả chính xác nội dung này.
Câu 16: v (Section C – Intelligent Lighting Systems)
- Dạng câu hỏi: Matching Headings
- Giải thích: Section C tập trung vào smart lighting systems với “daylight harvesting technologies that adjust artificial lighting based on available natural light”. Heading v “Lighting control systems adapting to natural conditions” paraphrase nội dung này.
Câu 17: iv (Section D – Energy Storage and Grid Integration)
- Dạng câu hỏi: Matching Headings
- Giải thích: Section D thảo luận về “energy storage systems” và cách chúng giải quyết challenges từ renewable energy sources. Heading iv “Storage solutions for renewable energy challenges” phù hợp hoàn toàn.
Câu 18: vi (Section E – Predictive Maintenance and Asset Management)
- Dạng câu hỏi: Matching Headings
- Giải thích: Section E nói về “predictive maintenance capabilities” và “continuously monitoring equipment performance” để ensure efficiency. Heading vi “Long-term equipment monitoring and efficiency” tóm tắt đúng nội dung.
Câu 19: i (Section F – Occupant Engagement and Behavioral Change)
- Dạng câu hỏi: Matching Headings
- Giải thích: Section F tập trung vào “human behavior remains a critical factor” và các strategies để engage occupants. Heading i “The role of human behavior in energy conservation” là lựa chọn chính xác.
Câu 20: NO
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Section F, dòng 1-2
- Giải thích: “While technology provides the tools for energy optimization, human behavior remains a critical factor” – Điều này mâu thuẫn với ý kiến rằng smart buildings “completely eliminate the need for human decision-making”.
Câu 21: YES
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Section B, dòng 1-2
- Giải thích: “Heating, ventilation, and air conditioning systems typically account for 40-60% of a building’s total energy consumption, making them the primary target” – Điều này khẳng định HVAC là largest consumer.
Câu 22: YES
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Section C, dòng 5-7
- Giải thích: “circadian lighting principles… which research suggests can enhance productivity and well-being” – Writer đồng ý với claim về productivity improvement.
Câu 23: NOT GIVEN
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Section C đề cập “some installations achieving payback periods of less than three years”
- Giải thích: Bài chỉ nói một số hệ thống có payback period dưới 3 năm, không đề cập đến việc tất cả systems pay for themselves within one year.
Câu 24: machine learning algorithms
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Section A, dòng 5-7
- Giải thích: “where machine learning algorithms identify patterns, detect anomalies, and recommend optimization strategies” – Đây là thành phần xử lý data được nhắc đến.
Câu 25: microclimatic zones
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Section B, dòng 6-7
- Giải thích: “create microclimatic zones that provide personalized comfort” – Thuật ngữ này xuất hiện nguyên văn trong bài.
Câu 26: demand response programs
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Section D, dòng 6-8
- Giải thích: “buildings can participate in demand response programs, automatically reducing energy consumption during grid stress events in exchange for financial incentives” – Cụm từ chính xác trong bài.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: contemporary smart buildings, operate
- Vị trí trong bài: Đoạn 2, toàn đoạn
- Giải thích: Đoạn 2 mô tả chi tiết “hierarchical control systems that operate across multiple temporal and spatial scales” với localized controllers, zone-level systems, building-level platforms, và portfolio-level analytics – tất cả operating at different timescales. Đáp án B chính xác.
Câu 28: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: limitation, rule-based control systems
- Vị trí trong bài: Đoạn 3, dòng 3-6
- Giải thích: “While transparent and predictable, such approaches struggle to address the multivariable optimization problems inherent in building management” – Đáp án B (cannot handle multiple competing objectives simultaneously) paraphrase chính xác limitation này.
Câu 29: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main concern, machine learning algorithms
- Vị trí trong bài: Đoạn 4, toàn đoạn
- Giải thích: Đoạn 4 nêu hai concerns chính: “limited insight into why specific decisions are made” (lack transparency) và “may perform poorly when confronted with novel conditions” (poor performance in unusual situations). Đáp án C bao quát cả hai vấn đề này.
Câu 30: D
- Dạng câu hỏi: Multiple Choice – EXCEPT
- Từ khóa: economic value, smart buildings
- Vị trí trong bài: Đoạn 5, toàn đoạn
- Giải thích: Đoạn 5 đề cập energy savings (A), improved occupant satisfaction/productivity (B), và extended asset lifespans (C). Không có thông tin nào về “decreased building construction time” (D) – đây là đáp án đúng cho câu hỏi EXCEPT.
Câu 31: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: traditional electrical grids
- Vị trí trong bài: Đoạn 7, dòng 4-7
- Giải thích: “The inflexibility of traditional buildings… necessitates maintaining substantial reserve generation capacity to meet peak demands that may occur only a few hours per year” – Đáp án B (require maintaining excess capacity for peak demand periods) paraphrase chính xác thông tin này.
Câu 32: D (Transactive energy frameworks)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 8, dòng 2-4
- Giải thích: “Transactive energy frameworks that enable buildings to respond dynamically to real-time price signals or direct grid service requests” – Mô tả này khớp với “adjust electricity usage based on real-time grid conditions”.
Câu 33: E (Rebound effect)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 9, dòng 6-7
- Giải thích: “the rebound effect – wherein efficiency improvements enable increased consumption rather than absolute reductions” – Định nghĩa này match với description về efficiency gains leading to increased consumption.
Câu 34: A (Neural networks)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 4, dòng 2-3
- Giải thích: “Neural networks and other black-box algorithms may achieve superior performance but provide limited insight into why specific decisions are made” – Đây là description về algorithms với good results nhưng little explanation.
Câu 35: C (Embodied energy)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 9, dòng 4-6
- Giải thích: “environmental assessments must consider the embodied energy and environmental impacts associated with manufacturing, installing, and eventually disposing of smart building technologies” – Embodied energy bao gồm impacts từ manufacturing và installing.
Câu 36: H (Portfolio-level analytics)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 2, dòng 9-11
- Giải thích: “portfolio-level analytics that aggregate data across multiple buildings, enabling comparative performance analysis, identification of best practices” – Đây chính xác là comparative analysis across multiple buildings.
Câu 37: reserve generation capacity
- Dạng câu hỏi: Short-answer Questions
- Vị trí trong bài: Đoạn 7, dòng 6-7
- Giải thích: “necessitates maintaining substantial reserve generation capacity to meet peak demands” – Cụm “reserve generation capacity” (3 từ) là đáp án chính xác.
Câu 38: cybersecurity vulnerabilities
- Dạng câu hỏi: Short-answer Questions
- Vị trí trong bài: Đoạn 8, dòng 5-6
- Giải thích: “Concerns about cybersecurity vulnerabilities associated with grid-connected building systems” – Cụm “cybersecurity vulnerabilities” (2 từ) xuất hiện nguyên văn.
Câu 39: 40% / forty percent
- Dạng câu hỏi: Short-answer Questions
- Vị trí trong bài: Đoạn 9, dòng 2
- Giải thích: “Buildings account for approximately 40% of global energy consumption” – Có thể viết “40%” hoặc “forty percent”.
Câu 40: embodied energy
- Dạng câu hỏi: Short-answer Questions
- Vị trí trong bài: Đoạn 9, dòng 4-5
- Giải thích: “environmental assessments must consider the embodied energy and environmental impacts associated with manufacturing, installing, and eventually disposing of smart building technologies” – “Embodied energy” (2 từ) là term mô tả energy và impacts từ production.
5. Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| optimize | v | /ˈɒptɪmaɪz/ | Tối ưu hóa | These intelligent structures… optimize energy consumption | optimize efficiency/performance/resources |
| rudimentary | adj | /ˌruːdɪˈmentri/ | Sơ khai, thô sơ | These rudimentary systems could adjust temperatures | rudimentary knowledge/system/form |
| automated | adj | /ˈɔːtəmeɪtɪd/ | Tự động hóa | Automated systems to make more informed decisions | automated process/system/response |
| allocate | v | /ˈæləkeɪt/ | Phân bổ, phân phối | About when and where to allocate resources | allocate resources/budget/time |
| conserve | v | /kənˈsɜːv/ | Bảo tồn, tiết kiệm | Thereby conserving energy without compromising comfort | conserve energy/water/resources |
| cohesive | adj | /kəʊˈhiːsɪv/ | Gắn kết, mạch lạc | Creating a more cohesive approach to building management | cohesive strategy/approach/unit |
| proactively | adv | /prəʊˈæktɪvli/ | Một cách chủ động | Can adjust settings proactively rather than reactively | respond proactively, act proactively |
| minimize | v | /ˈmɪnɪmaɪz/ | Giảm thiểu | Ensuring optimal conditions while minimizing wasted energy | minimize waste/risk/impact |
| integration | n | /ˌɪntɪˈɡreɪʃn/ | Sự tích hợp | Integration with renewable energy sources | system integration, full integration |
| substantial | adj | /səbˈstænʃl/ | Đáng kể, lớn | Can be substantial, often requiring several years | substantial amount/reduction/investment |
| retrofit | v | /ˈretrəʊfɪt/ | Cải tạo, lắp đặt thêm | Required to retrofit existing buildings with sensors | retrofit buildings/equipment/systems |
| emerging | adj | /ɪˈmɜːdʒɪŋ/ | Mới nổi, đang phát triển | Emerging technologies like digital twins | emerging markets/technologies/trends |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | Sự thay đổi mô hình căn bản | Represents a paradigm shift in how we approach resource conservation | undergo a paradigm shift |
| sophisticated | adj | /səˈfɪstɪkeɪtɪd/ | Tinh vi, phức tạp | Employ sophisticated algorithms to create dynamic environments | sophisticated system/technology/approach |
| granularity | n | /ˌɡrænjuˈlærəti/ | Tính chi tiết, mức độ cụ thể | The granularity of this data collection allows facility managers | data granularity, level of granularity |
| precondition | v | /ˌpriːkənˈdɪʃn/ | Điều hòa trước | These systems can precondition spaces before occupants arrive | precondition rooms/spaces |
| proliferation | n | /prəˌlɪfəˈreɪʃn/ | Sự gia tăng nhanh chóng | The proliferation of renewable energy sources | nuclear proliferation, rapid proliferation |
| inherently | adv | /ɪnˈherəntli/ | Vốn có, thuộc về bản chất | Solar and wind power generation is inherently variable | inherently risky/difficult/unstable |
| bidirectional | adj | /ˌbaɪdəˈrekʃənl/ | Hai chiều | This bidirectional relationship between buildings and the grid | bidirectional communication/flow |
| proactively | adv | /prəʊˈæktɪvli/ | Một cách chủ động, trước | Allowing maintenance to be scheduled proactively | respond proactively, manage proactively |
| lifecycle | n | /ˈlaɪfsaɪkl/ | Vòng đời | Ensures equipment operates at peak efficiency throughout its lifecycle | product lifecycle, lifecycle cost |
| tangible | adj | /ˈtændʒəbl/ | Hữu hình, rõ ràng | Can make abstract concepts tangible | tangible benefits/results/evidence |
| gamify | v | /ˈɡeɪmɪfaɪ/ | Trò chơi hóa | Some systems gamify energy conservation | gamify learning/experiences |
| benchmarking | n | /ˈbentʃmɑːkɪŋ/ | So sánh chuẩn mực | Performance benchmarking against similar buildings | competitive benchmarking, performance benchmarking |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| convergence | n | /kənˈvɜːdʒəns/ | Sự hội tụ | Represents a convergence of multiple technological trajectories | technological convergence, convergence of ideas |
| multifaceted | adj | /ˌmʌltiˈfæsɪtɪd/ | Nhiều mặt, đa chiều | Understanding these multifaceted impacts requires examining | multifaceted problem/approach/issue |
| hierarchical | adj | /ˌhaɪəˈrɑːkɪkl/ | Theo thứ bậc | Relies on hierarchical control systems | hierarchical structure/organization/system |
| granular | adj | /ˈɡrænjələ(r)/ | Chi tiết, cụ thể | At the most granular level, localized controllers manage | granular data/detail/control |
| latency | n | /ˈleɪtənsi/ | Độ trễ | Responding to immediate sensory inputs with millisecond-level latency | low latency, network latency |
| synthesize | v | /ˈsɪnθəsaɪz/ | Tổng hợp | Building-level management platforms that synthesize information | synthesize information/data/findings |
| transcend | v | /trænˈsend/ | Vượt qua | Identify optimization opportunities that transcend individual zones | transcend boundaries/limitations |
| nested | adj | /ˈnestɪd/ | Lồng nhau | This nested control architecture generates substantial complexity | nested structure/hierarchy/loops |
| non-linear | adj | /nɒn ˈlɪniə(r)/ | Phi tuyến tính | Discover complex, non-linear relationships within building data | non-linear relationship/growth/equation |
| algorithmic | adj | /ˌælɡəˈrɪðmɪk/ | Thuộc về thuật toán | Particularly regarding algorithmic transparency | algorithmic trading/decision/bias |
| black-box | adj | /blæk bɒks/ | Hộp đen (không minh bạch) | Neural networks and other black-box algorithms | black-box model/system/approach |
| catalyzed | v | /ˈkætəlaɪzd/ | Xúc tác, thúc đẩy | Has catalyzed innovation across multiple related industries | catalyze change/growth/innovation |
| spillover | n | /ˈspɪləʊvə(r)/ | Lan toa, tác động gián tiếp | This technology spillover generates economic value | spillover effect/benefits |
| unidirectional | adj | /ˌjuːnɪdəˈrekʃənl/ | Một chiều | Traditional electrical grids operate as fundamentally unidirectional systems | unidirectional flow/communication |
| inflexibility | n | /ɪnˌfleksəˈbɪləti/ | Tính không linh hoạt | The inflexibility of traditional buildings | organizational inflexibility, structural inflexibility |
| defer | v | /dɪˈfɜː(r)/ | Hoãn lại | Potentially deferring or eliminating the need for expensive infrastructure | defer payment/decision/maintenance |
| transactive | adj | /trænˈzæktɪv/ | Giao dịch, tương tác | Transactive energy frameworks that enable buildings to respond | transactive memory/system |
| embodied | adj | /ɪmˈbɒdid/ | Ẩn chứa, ngầm định | Must consider the embodied energy and environmental impacts | embodied carbon/energy/emissions |
| perennial | adj | /pəˈreniəl/ | Lâu dài, vĩnh viễn | The rebound effect presents a perennial concern | perennial problem/favorite/issue |
Kết bài
Chủ đề về vai trò của công nghệ thông minh trong hiệu quả năng lượng tòa nhà (Smart technology’s role in building energy efficiency) đang trở thành một trong những nội dung quan trọng và thường xuyên xuất hiện trong IELTS Reading. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ cả ba mức độ khó từ Easy đến Hard, với tổng cộng 40 câu hỏi thuộc nhiều dạng khác nhau – đúng như format của kỳ thi IELTS thực tế.
Ba passages trong đề thi đã cung cấp cho bạn cái nhìn toàn diện về sự phát triển của công nghệ tòa nhà thông minh từ quá khứ đến hiện tại, các ứng dụng thực tế của IoT và AI trong quản lý năng lượng, cũng như những tác động đa chiều của công nghệ này đối với hệ thống năng lượng đô thị. Độ phức tạp về từ vựng, cấu trúc câu và yêu cầu kỹ năng đọc hiểu tăng dần qua mỗi passage, giúp bạn làm quen với áp lực thời gian và nâng cao khả năng xử lý thông tin trong điều kiện thi thật.
Phần đáp án chi tiết với giải thích cụ thể về vị trí thông tin, cách paraphrase và kỹ thuật làm bài cho từng câu hỏi sẽ giúp bạn tự đánh giá mức độ của mình một cách chính xác. Đặc biệt, bảng từ vựng được tổng hợp theo từng passage không chỉ giúp bạn mở rộng vốn từ học thuật mà còn cung cấp collocations hữu ích có thể áp dụng trong cả phần thi Writing và Speaking.
Hãy dành thời gian xem lại những câu trả lời sai, phân tích kỹ giải thích để hiểu rõ lý do, và luyện tập thêm với các chủ đề tương tự để nâng cao band điểm Reading của bạn. Chúc bạn ôn tập hiệu quả và đạt được kết quả như mong muốn trong kỳ thi IELTS sắp tới!
