IELTS Reading: AI và Tích Hợp Năng Lượng Tái Tạo – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở bài

Chủ đề về trí tuệ nhân tạo (AI) và năng lượng tái tạo đang trở thành một trong những xu hướng nóng nhất trong các đề thi IELTS Reading hiện nay. Sự kết hợp giữa công nghệ AI và năng lượng sạch không chỉ phản ánh xu hướng phát triển toàn cầu mà còn thường xuyên xuất hiện trong các đề thi IELTS Academic từ năm 2020 đến nay, đặc biệt trong Passage 2 và Passage 3 với độ khó từ trung bình đến cao.

Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages được thiết kế theo đúng chuẩn Cambridge IELTS. Bạn sẽ được làm quen với đầy đủ 40 câu hỏi thuộc 7 dạng khác nhau, từ Multiple Choice, True/False/Not Given đến Matching Headings và Summary Completion. Mỗi câu hỏi đều đi kèm đáp án chi tiết với giải thích cụ thể về vị trí thông tin, kỹ thuật paraphrase và cách suy luận đúng đắn.

Đề thi này phù hợp cho học viên có trình độ từ band 5.0 trở lên, giúp bạn làm quen với nội dung học thuật về công nghệ và môi trường – hai chủ đề core trong IELTS. Bạn cũng sẽ học được hơn 40 từ vựng quan trọng liên quan đến AI, năng lượng tái tạo và bền vững môi trường, cùng với các collocations thường gặp trong bài thi thật.

Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút cho tổng cộng 3 passages với 40 câu hỏi. Đây là bài thi không có thời gian dành riêng để chép đáp án, vì vậy bạn cần quản lý thời gian vô cùng chặt chẽ.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó thấp, câu hỏi trực tiếp)
• Passage 2: 18-20 phút (độ khó trung bình, cần paraphrase)
• Passage 3: 23-25 phút (độ khó cao, cần suy luận sâu)

Lưu ý quan trọng: Mỗi passage có độ dài khoảng 700-900 từ và độ khó tăng dần. Bạn nên đọc kỹ instructions của từng dạng câu hỏi vì chúng quyết định format đáp án (số từ tối đa, có dùng số hay không…).

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm đầy đủ các dạng câu hỏi phổ biến nhất:

1. Multiple Choice – Chọn đáp án đúng từ 3-4 lựa chọn
2. True/False/Not Given – Xác định thông tin đúng, sai hay không được nhắc đến
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Sentence Completion – Hoàn thành câu với từ trong bài đọc
5. Matching Headings – Chọn tiêu đề phù hợp cho mỗi đoạn
6. Summary Completion – Điền từ vào đoạn tóm tắt
7. Short-answer Questions – Trả lời câu hỏi ngắn với từ trong bài

IELTS Reading Practice Test

PASSAGE 1 – The Dawn of Smart Energy Systems

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The integration of artificial intelligence into renewable energy systems represents one of the most significant technological advances of the 21st century. As the world grapples with climate change and the urgent need to reduce carbon emissions, the combination of AI and clean energy offers promising solutions that were unimaginable just a decade ago.

Solar power and wind energy have long been recognized as viable alternatives to fossil fuels, but they come with inherent challenges. Unlike traditional power plants that can adjust their output based on demand, renewable sources are intermittent – they produce electricity only when the sun shines or the wind blows. This unpredictability has historically made it difficult for power grid operators to rely heavily on these clean energy sources.

Enter artificial intelligence. Modern AI systems can analyze vast amounts of weather data, historical patterns, and real-time information to predict energy production with remarkable accuracy. For instance, machine learning algorithms can forecast solar panel output up to 48 hours in advance by processing satellite imagery, atmospheric pressure readings, and temperature trends. This predictive capability allows grid operators to plan more effectively and integrate higher percentages of renewable energy into their networks.

The impact of AI extends beyond prediction. Smart grid technology powered by AI can automatically balance supply and demand across entire regions. When a cloud passes over a solar farm, AI systems can instantly compensate by drawing power from other sources or releasing stored energy from batteries. This dynamic management happens in milliseconds, far faster than any human operator could respond.

Energy storage represents another area where AI is making substantial contributions. Battery systems are crucial for storing excess renewable energy for use during peak demand periods. AI algorithms optimize when to charge and discharge these batteries, considering factors such as electricity prices, weather forecasts, and consumption patterns. A study conducted in California showed that AI-managed battery systems were 23% more efficient than those operated using traditional methods.

Consumer behavior is also being transformed through AI-powered applications. Smart home devices can learn household routines and adjust energy consumption accordingly. For example, an AI system might pre-cool a home using solar power during the afternoon, reducing the need to draw from the grid during expensive evening peak hours. These seemingly small adjustments, when multiplied across millions of homes, can significantly reduce strain on power infrastructure.

The economic benefits are substantial. According to the International Energy Agency, AI optimization of renewable energy systems could save the global economy up to $80 billion annually by 2030. These savings come from reduced operational costs, improved equipment lifespan through predictive maintenance, and decreased reliance on expensive backup power from conventional plants.

However, implementing these systems is not without challenges. The initial investment in AI infrastructure and sensors can be considerable, particularly for developing nations. There are also concerns about data privacy and the security of interconnected energy networks. Cybersecurity experts warn that AI-managed grids could become targets for sophisticated attacks, potentially disrupting power supply to entire cities.

Despite these obstacles, the momentum behind AI-renewable energy integration continues to build. Countries like Denmark now generate over 50% of their electricity from wind power, thanks in large part to AI systems that manage this variable energy source. Similarly, Germany’s Energiewende (energy transition) program heavily relies on AI to coordinate thousands of small renewable energy installations across the country.

Looking ahead, researchers are exploring even more innovative applications. AI is being used to design more efficient solar panels by simulating millions of potential material combinations. In wind energy, AI algorithms analyze turbine performance data to optimize blade angles in real-time, increasing energy capture by up to 15%. Some pilot projects are even using AI to coordinate electric vehicle charging across cities, turning millions of car batteries into a distributed energy storage network.

The convergence of artificial intelligence and renewable energy is not just a technological evolution; it represents a fundamental shift in how humanity produces and consumes power. As AI systems become more sophisticated and renewable energy infrastructure expands, we move closer to a future where clean, reliable, and affordable electricity is available to everyone. This transformation may well prove to be one of the defining achievements of our era, addressing both our energy needs and environmental responsibilities simultaneously.

Questions 1-6

Do the following statements agree with the information given in Reading Passage 1?

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

1. Renewable energy sources produce electricity consistently throughout the day and night.

2. AI systems can predict solar energy production up to two days in advance.

3. Smart grid technology responds to changes in energy supply faster than human operators.

4. All countries have equal financial capacity to implement AI energy management systems.

5. Denmark produces more than half of its electricity from wind power.

6. Electric vehicles will completely replace traditional cars by 2030.

Questions 7-10

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

7. Unlike traditional power plants, renewable energy sources are __ because they depend on weather conditions.

8. AI algorithms help optimize battery systems by considering factors including __, weather predictions, and how people use energy.

9. The International Energy Agency estimates that AI optimization could save $80 billion per year by __.

10. Experts are concerned that AI-managed power grids might be vulnerable to __ attacks.

Questions 11-13

Choose the correct letter, A, B, C or D.

11. According to the passage, what is the main challenge with traditional renewable energy sources?

    • A) They are too expensive to install
    • B) They cannot produce enough electricity
    • C) Their energy production is unpredictable
    • D) They require too much maintenance

12. How much more efficient were AI-managed battery systems in the California study compared to traditional methods?

    • A) 15%
    • B) 23%
    • C) 48%
    • D) 50%

13. What is Germany’s Energiewende program primarily focused on?

    • A) Building new nuclear power plants
    • B) Exporting electricity to other countries
    • C) Transitioning to renewable energy sources
    • D) Reducing electricity consumption

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PASSAGE 2 – Algorithmic Optimization in Wind and Solar Farms

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The proliferation of renewable energy installations worldwide has created an unprecedented demand for sophisticated management systems capable of handling their inherent variability. While the first passage introduced the basic concepts of AI integration in clean energy, the technical sophistication and multifaceted challenges involved in large-scale implementation deserve deeper examination. Modern wind and solar farms are no longer simple collections of turbines or panels; they have evolved into complex ecosystems of sensors, processors, and predictive algorithms that work in concert to maximize efficiency.

Meteorological forecasting has traditionally been the domain of weather services, but renewable energy operators require precision that far exceeds conventional weather predictions. Standard weather forecasts might predict wind speeds with an accuracy of ±3 meters per second, which is adequate for planning daily activities but insufficient for optimizing turbine operations. AI-enhanced systems now achieve accuracies of ±0.5 meters per second for short-term predictions, utilizing ensemble models that combine data from multiple sources: ground-based sensors, atmospheric satellites, LIDAR (Light Detection and Ranging) systems, and historical performance data.

The sophistication of these predictions enables what engineers call “look-ahead optimization.” Rather than simply responding to current conditions, modern wind farms can adjust turbine settings based on anticipated changes in wind patterns minutes or even hours in advance. For instance, if the AI predicts a sharp increase in wind speed, it can prepare turbines by adjusting blade pitch angles and yaw positions proactively, ensuring they capture maximum energy the moment conditions change. Conversely, if dangerous wind speeds are forecast, turbines can be safely shut down in a coordinated sequence that minimizes mechanical stress and wear.

Solar energy systems benefit from similar AI-driven enhancements, though the challenges differ markedly from wind power. Photovoltaic efficiency is affected by numerous variables beyond simple sunlight intensity: panel temperature, dust accumulation, shading from clouds, and even the angle of incidence of sunlight on panels throughout the day. Machine learning models monitor these variables across thousands of panels simultaneously, identifying underperforming units that may require cleaning or maintenance.

One particularly innovative application involves what researchers term “cloud nowcasting” – predicting cloud movements and their impact on solar generation with extreme temporal precision. Traditional satellite imagery updates every 15-30 minutes, creating blind spots in solar forecasting. AI systems now incorporate ground-based sky cameras that capture images every few seconds, using computer vision algorithms to track cloud formations and predict exactly when and where shadows will fall across solar arrays. This granular information allows grid operators to anticipate production dips and prepare compensatory measures.

The concept of “virtual power plants” represents perhaps the most transformative application of AI in renewable energy. Rather than treating each wind farm or solar installation as an independent entity, AI systems can coordinate thousands of distributed energy resources as if they were a single, massive power station. When one solar farm experiences reduced output due to cloud cover, the system automatically requests increased output from wind turbines in another region, or draws from battery storage facilities, or even signals demand-side management systems to reduce consumption temporarily.

This orchestration requires processing enormous volumes of data in real-time. A typical virtual power plant might monitor inputs from 10,000+ sensors, updating its optimization calculations multiple times per second. The AI must balance multiple competing objectives: maximizing revenue, ensuring grid stability, respecting equipment limitations, and honoring contractual obligations with electricity buyers. Linear programming and other mathematical optimization techniques that would take days to solve using traditional computing methods can now be executed in milliseconds using specialized AI algorithms.

Predictive maintenance represents another critical area where AI delivers substantial value. Wind turbines operate in harsh environments, with components experiencing extreme mechanical loads and temperature variations. A single turbine blade failure can cost hundreds of thousands of dollars in repairs and lost production. AI systems continuously monitor vibration patterns, temperature readings, and acoustic signatures from turbine components, identifying anomalies that indicate impending failures long before they become critical. Studies suggest that AI-driven predictive maintenance reduces turbine downtime by 30-50% compared to traditional scheduled maintenance approaches.

The integration challenges, however, should not be underestimated. Legacy power grids were designed for unidirectional power flow from large centralized plants to consumers. Renewable energy creates bidirectional flows, with power sometimes moving from distributed sources back into transmission networks. AI systems must navigate complex regulatory frameworks that vary by jurisdiction, manage voltage fluctuations that can damage equipment, and ensure frequency stability across the grid.

Interoperability between different manufacturers’ equipment poses additional complications. A typical renewable energy installation might include turbines from one manufacturer, inverters from another, batteries from a third, and control systems from a fourth. Each component may use proprietary communication protocols, making unified AI management difficult. Industry groups are working toward standardized protocols, but progress has been slow, hindered by competitive concerns and technical disagreements.

Looking toward the future, researchers are exploring “self-learning grids” that continuously improve their performance without human intervention. These systems would use reinforcement learning – the same AI technique that enabled computers to master chess and Go – to discover optimal energy management strategies through trial and error. Early simulations suggest such systems could achieve efficiency improvements of 10-15% beyond current AI approaches, though practical implementation remains years away.

The economics of AI-enhanced renewable energy continue to improve as computing costs decline and algorithms become more efficient. Projects that were economically marginal a few years ago now deliver attractive returns on investment, encouraging further deployment. This creates a virtuous cycle: more installations generate more data, which trains better AI models, which improve performance, which encourages more installations. As this cycle accelerates, the vision of a fully renewable, AI-optimized energy system transitions from aspirational goal to practical reality.

Questions 14-18

Reading Passage 2 has eleven paragraphs, A-K.

Which paragraph contains the following information?

Write the correct letter, A-K.

NB: You may use any letter more than once.

14. A description of how AI systems manage multiple distributed energy sources as one unit

15. An explanation of the challenges created by equipment from different manufacturers

16. Information about the accuracy improvements AI provides for wind speed predictions

17. Details about how AI monitors turbine components to prevent equipment failure

18. A discussion of the financial improvements in AI renewable energy systems

Questions 19-23

Complete the summary below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

Modern renewable energy systems require much greater precision than traditional weather forecasting. AI systems can now predict wind speeds with accuracies of ±0.5 meters per second using (19) __ that integrate information from multiple sources. This precision enables (20) __, allowing wind farms to adjust turbine settings before conditions actually change. For solar power, AI addresses challenges including (21) __, dust, and cloud shadows through continuous monitoring. A particularly advanced technique called (22) ____ uses ground-based cameras to predict exactly when shadows will affect solar panels. AI systems must also handle (23) __ power flows, which differ from the traditional one-way movement of electricity from power plants to consumers.

Questions 24-26

Choose THREE letters, A-G.

Which THREE of the following are mentioned as benefits of AI in renewable energy systems?

A. Reduced electricity prices for all consumers
B. Better coordination between different energy sources
C. Elimination of all fossil fuel power plants
D. Decreased equipment downtime through early problem detection
E. Complete automation without human oversight
F. Faster processing of complex optimization calculations
G. Guaranteed prevention of all power outages

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PASSAGE 3 – The Nexus of Neural Networks and Sustainable Energy Futures

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The symbiotic relationship between artificial intelligence and renewable energy transcends mere technological advancement; it represents a paradigmatic shift in humanity’s approach to energy production, distribution, and consumption. This transformation is predicated not simply on substituting fossil fuels with cleaner alternatives, but on fundamentally reimagining the entire energy ecosystem through the lens of computational intelligence. The implications extend far beyond operational efficiency, touching upon geopolitical dynamics, economic structures, and the very feasibility of achieving carbon neutrality within the timeframes mandated by climate science.

Contemporary discourse on AI-renewable energy integration often overlooks the epistemological challenges inherent in managing systems of such staggering complexity. Modern power grids represent some of the most intricate machines ever constructed, with millions of interdependent components whose behaviors are governed by non-linear dynamics and subject to cascading failures. Traditional deterministic models prove inadequate when confronting the multi-dimensional uncertainty introduced by weather-dependent generation, fluctuating demand, and the increasing penetration of distributed energy resources. AI methodologies, particularly deep learning architectures, offer a radically different approach by learning patterns directly from data rather than relying on explicit mathematical formulations.

The convolutional neural networks (CNNs) employed in renewable energy forecasting exemplify this paradigm. Rather than requiring engineers to specify relationships between atmospheric variables and energy production explicitly, CNNs discover these relationships autonomously by processing historical data. These networks can identify subtle correlations that human analysts might overlook – for instance, the relationship between upper atmospheric jet stream patterns and ground-level wind conditions weeks later, or how aerosol concentrations affect diffuse solar radiation. A 2022 study published in Nature Energy demonstrated that CNN-based forecasting models reduced prediction errors by 40% compared to the best physics-based models, a margin that translates to billions of dollars in economic value when applied across global renewable energy installations.

However, the application of “black box” AI models in critical infrastructure raises profound questions about interpretability and accountability. When an AI system makes decisions affecting millions of people’s access to electricity, understanding why it made those decisions becomes paramount. If an algorithm instructs a grid operator to curtail wind farm output and the decision proves suboptimal, who bears responsibility? The AI developer? The utility company? The equipment manufacturer? This ambiguity has prompted research into “explainable AI” (XAI) techniques that can provide human-understandable justifications for their recommendations. Yet XAI remains an active research frontier, with current methods offering only partial transparency into complex model behavior.

The data infrastructure supporting AI-renewable integration presents its own constellation of challenges. Effective machine learning requires vast quantities of high-quality, representative data – a resource that remains unevenly distributed globally. Developed nations with long histories of digitized grid operations possess rich datasets that enable sophisticated AI applications, while developing countries often lack basic metering infrastructure. This “data divide” risks exacerbating global energy inequality, with advantaged nations leveraging AI to optimize their renewable systems while disadvantaged ones struggle with fundamental electrification. Some researchers advocate for “transfer learning” approaches that allow AI models trained on data-rich regions to be adapted for data-scarce contexts, though such techniques must account for significant differences in climate conditions, consumer behavior, and infrastructure characteristics.

Cybersecurity vulnerabilities constitute perhaps the most insidious risk associated with AI-managed energy systems. The same interconnectivity that enables coordinated optimization creates attack vectors for malicious actors. A successful cyber intrusion into AI control systems could manipulate energy flows, potentially causing blackouts, damaging equipment, or even creating physical dangers through voltage instabilities. The 2015 attack on Ukraine’s power grid, while not AI-related, demonstrated the catastrophic potential of energy system cyberwarfare. As grids become more dependent on AI, the sophistication required for such attacks decreases while their potential impact amplifies. Adversarial machine learning – techniques for fooling AI systems through carefully crafted inputs – poses particularly insidious threats, as attackers might cause AI systems to make catastrophically bad decisions without directly accessing control systems.

The temporal dynamics of AI development present additional strategic considerations. AI capabilities evolve at exponential rates, with models that achieve state-of-the-art performance today becoming obsolete within months. Energy infrastructure, conversely, operates on decadal timescales, with major investments expected to remain operational for 20-40 years. This temporal mismatch creates a dilemma: should utilities invest in current AI technologies knowing they will soon be surpassed, or wait for more advanced systems while forgoing present benefits? Moreover, institutional inertia in the energy sector – driven by regulatory constraints, risk-averse cultures, and entrenched interests – often prevents rapid adoption even when technologies are mature. Successful integration requires not merely technical solutions but organizational transformation and regulatory evolution.

Economic modeling of AI-renewable integration reveals counterintuitive dynamics. One might assume that AI optimization simply improves financial returns for renewable energy operators, creating unambiguous economic incentives for adoption. Reality proves more complex. In liberalized electricity markets, increased efficiency can paradoxically reduce revenues through a mechanism economists term the “efficiency paradox.” When AI enables renewable sources to produce more predictably and reliably, their electricity becomes less valuable in markets that prize flexibility. Furthermore, if all operators adopt similar AI systems, their collectively optimized behavior might create new forms of market instability or emergent patterns that no individual operator intends. Game-theoretic analyses suggest that strategic interactions between AI-managed systems could lead to inefficient equilibria unless carefully regulated.

The environmental implications extend beyond the obvious benefits of increased renewable energy deployment. Training large AI models requires substantial computational resources, with recent estimates suggesting that training a single large language model can generate carbon emissions equivalent to five cars’ lifetime use. While the AI systems used in energy management are generally smaller and less computationally intensive, the aggregate impact of millions of models being continuously trained and updated deserves scrutiny. Some critics argue that we must account for AI’s carbon footprint when calculating the net environmental benefits of AI-renewable integration, though quantifying this precisely remains methodologically challenging given varying electricity sources powering data centers and differing algorithmic efficiencies.

Looking toward multi-decadal horizons, the convergence of AI and renewable energy may enable energy systems fundamentally different from anything previously imagined. Researchers envision “autonomous grids” that require minimal human oversight, self-healing networks that automatically reconfigure around component failures, and prediction systems so accurate that energy storage requirements drop dramatically. Some futurists speculate about AI systems that design improved renewable energy technologies themselves, creating a recursive improvement cycle where AI-designed systems generate data that trains more capable AI, which designs even better systems. Whether such visions prove realistic or remain science fiction will depend on navigating the technical, economic, social, and political challenges outlined above.

The integration of artificial intelligence into renewable energy systems is not merely an engineering challenge but a civilizational undertaking that will shape energy access, economic development, and environmental outcomes for generations. Success requires not only technical innovation but also thoughtful consideration of equity, security, transparency, and governance. As these systems become increasingly integral to modern life, ensuring they serve broad social interests rather than narrow commercial objectives becomes paramount. The path forward demands interdisciplinary collaboration bringing together engineers, policymakers, ethicists, and communities to collectively navigate the opportunities and pitfalls of this transformative technology convergence.

Questions 27-31

Choose the correct letter, A, B, C or D.

27. According to the passage, what is the primary advantage of convolutional neural networks over traditional forecasting methods?

    • A) They require less computational power to operate
    • B) They can identify patterns without explicit programming
    • C) They are easier for non-experts to understand
    • D) They work better in developing countries

28. The “efficiency paradox” mentioned in the passage refers to the situation where:

    • A) AI systems use too much electricity
    • B) Improved predictability reduces market value
    • C) Renewable energy becomes too expensive
    • D) Markets become completely unstable

29. What does the passage suggest about the “data divide”?

    • A) It is a temporary problem that will soon be resolved
    • B) It only affects countries without renewable energy
    • C) It could worsen global energy inequality
    • D) It can be easily solved through transfer learning

30. The 2015 Ukraine power grid attack is mentioned to illustrate:

    • A) Why AI systems are inherently dangerous
    • B) The potential consequences of energy system cyber attacks
    • C) How AI prevented a major disaster
    • D) Why renewable energy is more secure than fossil fuels

31. What challenge does the passage identify regarding AI development timescales?

    • A) AI develops too slowly for energy sector needs
    • B) Energy infrastructure has much longer operational lifespans than AI systems
    • C) Regulatory frameworks evolve faster than technology
    • D) Companies invest too much in outdated AI technologies

Questions 32-36

Complete the summary using the list of phrases, A-K, below.

The integration of AI into renewable energy systems presents numerous challenges beyond technical implementation. One major concern involves the (32) __ of complex AI models, which makes it difficult to understand how decisions are made. This has led to research into explainable AI techniques. Additionally, training large AI models creates a significant (33) __, which must be considered when calculating environmental benefits. The (34) __ between AI-managed systems could also lead to unexpected market behaviors. Furthermore, the same interconnected networks that enable optimization also create (35) ____ for malicious actors. Finally, there is a mismatch between the **(36) __ of AI development and the much longer operational periods of energy infrastructure.

A. carbon footprint
B. economic incentives
C. lack of interpretability
D. rapid evolution
E. strategic interactions
F. physical dangers
G. attack vectors
H. regulatory constraints
I. training requirements
J. prediction accuracy
K. financial returns

Questions 37-40

Do the following statements agree with the claims of the writer in Reading Passage 3?

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

37. Traditional deterministic models are adequate for managing modern renewable energy systems.

38. Explainable AI techniques currently provide complete transparency into how complex models make decisions.

39. The environmental benefits of AI-renewable integration should be calculated after accounting for AI’s own carbon emissions.

40. Autonomous grids requiring minimal human oversight will definitely be achieved within the next decade.

Hệ thống lưới điện thông minh tích hợp trí tuệ nhân tạo và năng lượng tái tạo trong IELTS Reading

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Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. FALSE
2. TRUE
3. TRUE
4. NOT GIVEN
5. TRUE
6. NOT GIVEN
7. intermittent
8. electricity prices
9. 2030
10. cyber(security)
11. C
12. B
13. C

PASSAGE 2: Questions 14-26

14. F
15. J
16. B
17. H
18. K
19. ensemble models
20. look-ahead optimization
21. panel temperature
22. cloud nowcasting
23. bidirectional
24. B
25. D
26. F

PASSAGE 3: Questions 27-40

27. B
28. B
29. C
30. B
31. B
32. C
33. A
34. E
35. G
36. D
37. NO
38. NO
39. YES
40. NOT GIVEN

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Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: renewable energy sources, produce electricity, consistently, day and night
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Bài đọc nói rõ “Unlike traditional power plants that can adjust their output based on demand, renewable sources are intermittent – they produce electricity only when the sun shines or the wind blows.” Câu hỏi nói năng lượng tái tạo sản xuất điện “consistently” (liên tục) còn bài đọc nói chúng “intermittent” (không liên tục), do đó câu này FALSE.

Câu 2: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI systems, predict, solar energy production, two days in advance
• Vị trí trong bài: Đoạn 3, dòng 3-5
• Giải thích: Bài đọc nói “machine learning algorithms can forecast solar panel output up to 48 hours in advance”. 48 giờ = 2 ngày, do đó câu này TRUE. Đây là ví dụ về paraphrase số liệu.

Câu 3: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: smart grid technology, responds, faster than, human operators
• Vị trí trong bài: Đoạn 4, dòng 3-4
• Giải thích: Bài đọc nói “This dynamic management happens in milliseconds, far faster than any human operator could respond.” Rõ ràng công nghệ phản ứng nhanh hơn con người.

Câu 4: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: all countries, equal financial capacity, implement AI
• Vị trí trong bài: Đoạn 8 có đề cập đến chi phí
• Giải thích: Bài đọc chỉ nói “The initial investment in AI infrastructure and sensors can be considerable, particularly for developing nations” nhưng không so sánh khả năng tài chính của TẤT CẢ các quốc gia hay nói họ có năng lực ngang nhau. Do đó là NOT GIVEN.

Câu 5: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Denmark, produces, more than half, electricity, wind power
• Vị trí trong bài: Đoạn 9, dòng 2-3
• Giải thích: “Countries like Denmark now generate over 50% of their electricity from wind power” – “over 50%” = “more than half”, do đó TRUE.

Câu 6: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: electric vehicles, completely replace, traditional cars, 2030
• Vị trí trong bài: Đoạn 10 có đề cập xe điện
• Giải thích: Bài chỉ nói về việc sử dụng xe điện để lưu trữ năng lượng, không hề đề cập đến việc chúng sẽ thay thế hoàn toàn xe truyền thống vào năm 2030.

Câu 7: intermittent

• Dạng câu hỏi: Sentence Completion
• Từ khóa: renewable energy sources, depend on weather
• Vị trí trong bài: Đoạn 2, dòng 2-3
• Giải thích: “renewable sources are intermittent – they produce electricity only when the sun shines or the wind blows” – từ cần điền là “intermittent” vì nó mô tả đặc điểm không ổn định của năng lượng tái tạo.

Câu 8: electricity prices

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI algorithms, optimize battery systems, considering factors
• Vị trí trong bài: Đoạn 5, dòng 3-4
• Giải thích: “AI algorithms optimize when to charge and discharge these batteries, considering factors such as electricity prices, weather forecasts, and consumption patterns.” Các yếu tố bao gồm electricity prices (giá điện).

Câu 9: 2030

• Dạng câu hỏi: Sentence Completion
• Từ khóa: International Energy Agency, save $80 billion per year
• Vị trí trong bài: Đoạn 7, dòng 1-2
• Giải thích: “AI optimization of renewable energy systems could save the global economy up to $80 billion annually by 2030” – mốc thời gian là 2030.

Câu 10: cyber/cybersecurity

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI-managed grids, vulnerable, attacks
• Vị trí trong bài: Đoạn 8, dòng 3-4
• Giải thích: “Cybersecurity experts warn that AI-managed grids could become targets for sophisticated attacks” – loại tấn công là cyber attacks (tấn công mạng).

Câu 11: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main challenge, traditional renewable energy sources
• Vị trí trong bài: Đoạn 2
• Giải thích: Đoạn 2 giải thích rõ thách thức chính là tính không ổn định: “This unpredictability has historically made it difficult…” Đáp án C “Their energy production is unpredictable” chính xác nhất.

Câu 12: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: California study, AI-managed battery systems, efficient
• Vị trí trong bài: Đoạn 5, dòng 4-5
• Giải thích: “A study conducted in California showed that AI-managed battery systems were 23% more efficient” – đáp án là B.

Câu 13: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Germany’s Energiewende program
• Vị trí trong bài: Đoạn 9, dòng 3-4
• Giải thích: “Germany’s Energiewende (energy transition) program” – từ “energy transition” chỉ rõ đây là chương trình chuyển đổi sang năng lượng tái tạo, đáp án C đúng.

Trang trại điện gió và năng lượng mặt trời được quản lý bởi hệ thống AI hiện đại

Passage 2 – Giải Thích

Câu 14: F

• Dạng câu hỏi: Matching Information
• Từ khóa: manage multiple distributed energy sources as one unit
• Vị trí trong bài: Đoạn F (đoạn 6)
• Giải thích: Đoạn F nói về “virtual power plants” – “AI systems can coordinate thousands of distributed energy resources as if they were a single, massive power station.” Đây chính là việc quản lý nhiều nguồn năng lượng phân tán như một đơn vị.

Câu 15: J

• Dạng câu hỏi: Matching Information
• Từ khóa: challenges, equipment from different manufacturers
• Vị trí trong bài: Đoạn J (đoạn 10)
• Giải thích: Đoạn J nói về “Interoperability between different manufacturers’ equipment poses additional complications” và giải thích chi tiết về vấn đề thiết bị từ các nhà sản xuất khác nhau.

Câu 16: B

• Dạng câu hỏi: Matching Information
• Từ khóa: accuracy improvements, wind speed predictions
• Vị trí trong bài: Đoạn B (đoạn 2)
• Giải thích: Đoạn B nói “AI-enhanced systems now achieve accuracies of ±0.5 meters per second” so với “±3 meters per second” của dự báo thông thường – đây là cải thiện độ chính xác.

Câu 17: H

• Dạng câu hỏi: Matching Information
• Từ khóa: AI monitors turbine components, prevent equipment failure
• Vị trí trong bài: Đoạn H (đoạn 8)
• Giải thích: Đoạn H nói về “Predictive maintenance” – “AI systems continuously monitor vibration patterns, temperature readings, and acoustic signatures from turbine components, identifying anomalies that indicate impending failures.”

Câu 18: K

• Dạng câu hỏi: Matching Information
• Từ khóa: financial improvements, AI renewable energy systems
• Vị trí trong bài: Đoạn K (đoạn 12)
• Giải thích: Đoạn K nói “The economics of AI-enhanced renewable energy continue to improve” và thảo luận về cải thiện tài chính.

Câu 19: ensemble models

• Dạng câu hỏi: Summary Completion
• Từ khóa: predict wind speeds, integrate information, multiple sources
• Vị trí trong bài: Đoạn 2, dòng 4-5
• Giải thích: “utilizing ensemble models that combine data from multiple sources” – ensemble models là công cụ tích hợp dữ liệu từ nhiều nguồn.

Câu 20: look-ahead optimization

• Dạng câu hỏi: Summary Completion
• Từ khóa: adjust turbine settings before conditions change
• Vị trí trong bài: Đoạn 3, dòng 1-2
• Giải thích: “The sophistication of these predictions enables what engineers call ‘look-ahead optimization’.” Kỹ thuật này cho phép điều chỉnh trước khi điều kiện thay đổi.

Câu 21: panel temperature

• Dạng câu hỏi: Summary Completion
• Từ khóa: challenges, solar power, including
• Vị trí trong bài: Đoạn 4, dòng 2-3
• Giải thích: “Photovoltaic efficiency is affected by numerous variables beyond simple sunlight intensity: panel temperature, dust accumulation, shading from clouds…”

Câu 22: cloud nowcasting

• Dạng câu hỏi: Summary Completion
• Từ khóa: advanced technique, ground-based cameras, predict shadows
• Vị trí trong bài: Đoạn 5, dòng 1-2
• Giải thích: “One particularly innovative application involves what researchers term ‘cloud nowcasting’ – predicting cloud movements and their impact on solar generation.”

Câu 23: bidirectional

• Dạng câu hỏi: Summary Completion
• Từ khóa: power flows, differ from traditional one-way movement
• Vị trí trong bài: Đoạn 9, dòng 2-3
• Giải thích: “Legacy power grids were designed for unidirectional power flow from large centralized plants to consumers. Renewable energy creates bidirectional flows.”

Câu 24-26: B, D, F

• Dạng câu hỏi: Multiple Choice (chọn 3 đáp án)
• Giải thích:
  • B (Better coordination): Đoạn 6 nói về virtual power plants phối hợp nhiều nguồn năng lượng
  • D (Decreased downtime): Đoạn 8 nói “AI-driven predictive maintenance reduces turbine downtime by 30-50%”
  • F (Faster processing): Đoạn 7 nói “optimization calculations” có thể thực hiện “in milliseconds”
  • Các đáp án khác không được đề cập hoặc sai: A (giá điện cho tất cả người tiêu dùng), C (loại bỏ hoàn toàn nhiên liệu hóa thạch), E (tự động hóa hoàn toàn không cần người), G (đảm bảo ngăn chặn tất cả sự cố)

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: convolutional neural networks, primary advantage
• Vị trí trong bài: Đoạn 3, dòng 1-3
• Giải thích: “Rather than requiring engineers to specify relationships between atmospheric variables and energy production explicitly, CNNs discover these relationships autonomously by processing historical data.” Ưu điểm chính là tự động nhận diện patterns mà không cần lập trình tường minh – đáp án B.

Câu 28: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: efficiency paradox
• Vị trí trong bài: Đoạn 8, dòng 4-6
• Giải thích: “When AI enables renewable sources to produce more predictably and reliably, their electricity becomes less valuable in markets that prize flexibility.” Nghịch lý là độ tin cậy tăng làm giảm giá trị thị trường – đáp án B.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: data divide
• Vị trí trong bài: Đoạn 5, dòng 4-6
• Giải thích: “This ‘data divide’ risks exacerbating global energy inequality, with advantaged nations leveraging AI to optimize their renewable systems while disadvantaged ones struggle.” Bài nói khoảng cách dữ liệu có thể làm trầm trọng thêm bất bình đẳng năng lượng – đáp án C.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: 2015 Ukraine power grid attack
• Vị trí trong bài: Đoạn 6, dòng 4-5
• Giải thích: “The 2015 attack on Ukraine’s power grid, while not AI-related, demonstrated the catastrophic potential of energy system cyberwarfare.” Ví dụ này minh họa hậu quả nghiêm trọng của tấn công mạng vào hệ thống năng lượng – đáp án B.

Câu 31: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI development timescales, challenge
• Vị trí trong bài: Đoạn 7, dòng 1-3
• Giải thích: “AI capabilities evolve at exponential rates… Energy infrastructure, conversely, operates on decadal timescales, with major investments expected to remain operational for 20-40 years. This temporal mismatch creates a dilemma.” Thách thức là tuổi thọ cơ sở hạ tầng dài hơn nhiều so với chu kỳ phát triển AI – đáp án B.

Câu 32: C (lack of interpretability)

• Dạng câu hỏi: Summary Completion with word list
• Vị trí trong bài: Đoạn 4, dòng 1-2
• Giải thích: “the application of ‘black box’ AI models in critical infrastructure raises profound questions about interpretability and accountability.” Vấn đề chính là thiếu khả năng giải thích.

Câu 33: A (carbon footprint)

• Dạng câu hỏi: Summary Completion with word list
• Vị trí trong bài: Đoạn 9, dòng 1-3
• Giải thích: “Training large AI models requires substantial computational resources, with recent estimates suggesting that training a single large language model can generate carbon emissions…” Việc huấn luyện AI tạo ra carbon footprint đáng kể.

Câu 34: E (strategic interactions)

• Dạng câu hỏi: Summary Completion with word list
• Vị trí trong bài: Đoạn 8, dòng 6-7
• Giải thích: “Game-theoretic analyses suggest that strategic interactions between AI-managed systems could lead to inefficient equilibria.” Tương tác chiến lược giữa các hệ thống AI có thể gây ra hành vi thị trường không mong đợi.

Câu 35: G (attack vectors)

• Dạng câu hỏi: Summary Completion with word list
• Vị trí trong bài: Đoạn 6, dòng 1-2
• Giải thích: “The same interconnectivity that enables coordinated optimization creates attack vectors for malicious actors.” Mạng kết nối tạo ra các điểm tấn công cho kẻ xấu.

Câu 36: D (rapid evolution)

• Dạng câu hỏi: Summary Completion with word list
• Vị trí trong bài: Đoạn 7, dòng 1
• Giải thích: “AI capabilities evolve at exponential rates, with models that achieve state-of-the-art performance today becoming obsolete within months.” Sự phát triển nhanh của AI tạo ra mismatch với cơ sở hạ tầng năng lượng.

Câu 37: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: traditional deterministic models, adequate, modern renewable energy
• Vị trí trong bài: Đoạn 2, dòng 3-4
• Giải thích: “Traditional deterministic models prove inadequate when confronting the multi-dimensional uncertainty introduced by weather-dependent generation…” Tác giả rõ ràng cho rằng các mô hình truyền thống KHÔNG đủ (inadequate).

Câu 38: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: explainable AI, complete transparency
• Vị trí trong bài: Đoạn 4, dòng 6-7
• Giải thích: “Yet XAI remains an active research frontier, with current methods offering only partial transparency into complex model behavior.” Tác giả nói XAI chỉ cung cấp “partial transparency” (minh bạch một phần) chứ không phải “complete transparency”.

Câu 39: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: environmental benefits, calculated after accounting, AI’s carbon emissions
• Vị trí trong bài: Đoạn 9, dòng 4-5
• Giải thích: “Some critics argue that we must account for AI’s carbon footprint when calculating the net environmental benefits of AI-renewable integration.” Tác giả đồng ý với quan điểm này (sử dụng động từ “must”).

Câu 40: NOT GIVEN

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: autonomous grids, definitely achieved, next decade
• Vị trí trong bài: Đoạn 10, dòng 1-3
• Giải thích: Bài đọc nói “Researchers envision ‘autonomous grids’… Whether such visions prove realistic or remain science fiction will depend on navigating the technical, economic, social, and political challenges.” Tác giả không khẳng định chắc chắn điều này sẽ xảy ra trong thập kỷ tới.

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Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
integration	n	/ˌɪntɪˈɡreɪʃn/	Sự tích hợp, hợp nhất	The integration of AI into renewable energy systems	system integration, full integration
intermittent	adj	/ˌɪntəˈmɪtənt/	Gián đoạn, không liên tục	Renewable sources are intermittent	intermittent supply, intermittent problem
unpredictability	n	/ˌʌnprɪˌdɪktəˈbɪləti/	Tính không thể dự đoán	This unpredictability has historically made it difficult	weather unpredictability, market unpredictability
compensate	v	/ˈkɒmpenseɪt/	Bù đắp, đền bù	AI systems can instantly compensate by drawing power	compensate for losses, fully compensate
dynamic	adj	/daɪˈnæmɪk/	Năng động, linh hoạt	This dynamic management happens in milliseconds	dynamic system, dynamic response
optimize	v	/ˈɒptɪmaɪz/	Tối ưu hóa	AI algorithms optimize when to charge batteries	optimize performance, optimize efficiency
substantial	adj	/səbˈstænʃl/	Đáng kể, lớn	The economic benefits are substantial	substantial savings, substantial impact
infrastructure	n	/ˈɪnfrəstrʌktʃə(r)/	Cơ sở hạ tầng	Reduce strain on power infrastructure	energy infrastructure, critical infrastructure
predictive	adj	/prɪˈdɪktɪv/	Thuộc về dự đoán	Predictive maintenance of equipment	predictive model, predictive analytics
variable	adj	/ˈveəriəbl/	Thay đổi, biến thiên	AI systems that manage this variable energy source	variable weather, variable output
innovative	adj	/ˈɪnəvətɪv/	Sáng tạo, đổi mới	Researchers are exploring even more innovative applications	innovative solution, innovative approach
transformation	n	/ˌtrænsfəˈmeɪʃn/	Sự chuyển đổi, biến đổi	This transformation may well prove to be one of the defining achievements	digital transformation, energy transformation

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
proliferation	n	/prəˌlɪfəˈreɪʃn/	Sự gia tăng nhanh chóng	The proliferation of renewable energy installations	nuclear proliferation, rapid proliferation
inherent	adj	/ɪnˈhɪərənt/	Vốn có, cố hữu	Their inherent variability	inherent risk, inherent problem
multifaceted	adj	/ˌmʌltiˈfæsɪtɪd/	Nhiều mặt, đa diện	Multifaceted challenges involved	multifaceted approach, multifaceted issue
meteorological	adj	/ˌmiːtiərəˈlɒdʒɪkl/	Thuộc về khí tượng	Meteorological forecasting	meteorological data, meteorological station
ensemble	n/adj	/ɒnˈsɒmbl/	Tập hợp, toàn bộ	Utilizing ensemble models	ensemble method, ensemble learning
proactively	adv	/prəʊˈæktɪvli/	Một cách chủ động	Adjust turbine settings proactively	act proactively, respond proactively
orchestration	n	/ˌɔːkɪˈstreɪʃn/	Sự điều phối, phối hợp	This orchestration requires processing enormous volumes	careful orchestration, complex orchestration
anomaly	n	/əˈnɒməli/	Bất thường, dị thường	Identifying anomalies that indicate impending failures	detect anomaly, temperature anomaly
bidirectional	adj	/ˌbaɪdəˈrekʃənl/	Hai chiều	Renewable energy creates bidirectional flows	bidirectional flow, bidirectional communication
interoperability	n	/ˌɪntərˌɒpərəˈbɪləti/	Khả năng tương tác	Interoperability between different manufacturers’ equipment	ensure interoperability, system interoperability
proprietary	adj	/prəˈpraɪətri/	Độc quyền, riêng biệt	Use proprietary communication protocols	proprietary technology, proprietary software
reinforcement learning	n	/ˌriːɪnˈfɔːsmənt ˈlɜːnɪŋ/	Học tăng cường (AI)	Use reinforcement learning to discover optimal strategies	deep reinforcement learning, reinforcement learning algorithm
virtuous cycle	n	/ˈvɜːtʃuəs ˈsaɪkl/	Vòng tuần hoàn tích cực	This creates a virtuous cycle	create virtuous cycle, establish virtuous cycle
marginal	adj	/ˈmɑːdʒɪnl/	Cận biên, tối thiểu	Projects that were economically marginal	marginal cost, marginal profit

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
symbiotic	adj	/ˌsɪmbaɪˈɒtɪk/	Cộng sinh	The symbiotic relationship between AI and renewable energy	symbiotic relationship, symbiotic partnership
paradigmatic	adj	/ˌpærədɪɡˈmætɪk/	Thuộc về mô hình, điển hình	Represents a paradigmatic shift	paradigmatic change, paradigmatic example
epistemological	adj	/ɪˌpɪstɪməˈlɒdʒɪkl/	Thuộc về nhận thức luận	Epistemological challenges	epistemological question, epistemological framework
staggering	adj	/ˈstæɡərɪŋ/	Choáng ngợp, khổng lồ	Systems of such staggering complexity	staggering amount, staggering scale
non-linear	adj	/nɒn ˈlɪniə(r)/	Phi tuyến	Governed by non-linear dynamics	non-linear relationship, non-linear system
cascading	adj	/kæsˈkeɪdɪŋ/	Liên hoàn, dây chuyền	Subject to cascading failures	cascading effect, cascading crisis
penetration	n	/ˌpenɪˈtreɪʃn/	Sự thâm nhập, xâm nhập	Increasing penetration of distributed energy resources	market penetration, deep penetration
convolutional	adj	/ˌkɒnvəˈluːʃənl/	Tích chập (trong AI)	Convolutional neural networks (CNNs)	convolutional layer, convolutional network
autonomously	adv	/ɔːˈtɒnəməsli/	Một cách tự động	CNNs discover relationships autonomously	operate autonomously, function autonomously
interpretability	n	/ɪnˌtɜːprɪtəˈbɪləti/	Khả năng giải thích được	Questions about interpretability and accountability	model interpretability, improve interpretability
accountability	n	/əˌkaʊntəˈbɪləti/	Trách nhiệm giải trình	Questions about interpretability and accountability	ensure accountability, corporate accountability
constellation	n	/ˌkɒnstəˈleɪʃn/	Chòm sao, tập hợp	A constellation of challenges	constellation of factors, constellation of issues
insidious	adj	/ɪnˈsɪdiəs/	Ngấm ngầm, âm hiểm	The most insidious risk	insidious threat, insidious disease
adversarial	adj	/ˌædvəˈseəriəl/	Đối nghịch, thù địch	Adversarial machine learning	adversarial attack, adversarial example
temporal	adj	/ˈtempərəl/	Thuộc về thời gian	The temporal dynamics of AI development	temporal dimension, temporal pattern
exponential	adj	/ˌekspəˈnenʃl/	Mũ, theo hàm số mũ	AI capabilities evolve at exponential rates	exponential growth, exponential increase
paradoxically	adv	/ˌpærəˈdɒksɪkli/	Một cách nghịch lý	Increased efficiency can paradoxically reduce revenues	paradoxically, this creates…
recursive	adj	/rɪˈkɜːsɪv/	Đệ quy, lặp lại	A recursive improvement cycle	recursive process, recursive function

Chuyên gia giảng dạy IELTS Reading về công nghệ AI và năng lượng tái tạo

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Kết Bài

Chủ đề “AI và tích hợp năng lượng tái tạo” không chỉ là một nội dung học thuật quan trọng mà còn phản ánh xu hướng toàn cầu về công nghệ và bảo vệ môi trường. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba mức độ khó từ cơ bản đến nâng cao, giúp bạn hiểu rõ cách IELTS Reading test được thiết kế và đánh giá.

Passage 1 cung cấp nền tảng về cách AI đang thay đổi cách chúng ta sử dụng năng lượng tái tạo, với từ vựng và cấu trúc câu dễ tiếp cận. Passage 2 đi sâu vào các ứng dụng kỹ thuật cụ thể như dự báo khí tượng, tối ưu hóa tuabin gió và trang trại năng lượng mặt trời, yêu cầu khả năng hiểu và phân tích ở mức độ cao hơn. Passage 3 thách thức bạn với nội dung học thuật phức tạp về các vấn đề triết học, kinh tế và an ninh mạng liên quan đến AI, đòi hỏi kỹ năng suy luận và phân tích sâu sắc.

Với 40 câu hỏi thuộc 7 dạng khác nhau, bạn đã thực hành toàn diện các kỹ năng cần thiết: tìm thông tin chi tiết (scanning), hiểu ý chính (skimming), phân biệt True/False/Not Given, nối thông tin, và hoàn thành câu. Phần giải thích đáp án chi tiết không chỉ cho bạn biết đáp án đúng mà còn hướng dẫn cách tư duy và cách áp dụng kỹ thuật paraphrase – kỹ năng quan trọng nhất trong IELTS Reading.

Hơn 40 từ vựng được trình bày theo bảng chi tiết với phiên âm, nghĩa, ví dụ và collocations sẽ giúp bạn mở rộng vốn từ học thuật, đặc biệt trong lĩnh vực công nghệ và môi trường – hai chủ đề thường xuyên xuất hiện trong đề thi IELTS. Hãy dành thời gian học kỹ những từ này và luyện tập sử dụng chúng trong ngữ cảnh.

Để đạt kết quả tốt nhất, bạn nên làm lại đề thi này sau một tuần với giới hạn thời gian chặt chẽ 60 phút, sau đó so sánh kết quả để đánh giá sự tiến bộ. Hãy nhớ rằng, IELTS Reading không chỉ kiểm tra khả năng đọc hiểu mà còn đánh giá kỹ năng quản lý thời gian và chiến lược làm bài. Chúc bạn luyện tập hiệu quả và đạt band điểm mong muốn!