Artificial intelligence-based solutions for climate change: a review

Environmental Chemistry Letters, Journal Year: 2023, Volume and Issue: 21(5), P. 2525 - 2557

Published: June 13, 2023

Abstract Climate change is a major threat already causing system damage to urban and natural systems, inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because integrates internet resources make prompt suggestions based on accurate climate predictions. Here we review recent research applications in mitigating the adverse effects change, with focus energy efficiency, carbon sequestration storage, weather renewable forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, resilient cities. We found that enhancing efficiency can significantly contribute impact change. Smart manufacturing reduce consumption, waste, emissions 30–50% and, particular, consumption buildings 30–50%. About 70% gas industry utilizes technologies enhance accuracy reliability forecasts. Combining smart grids optimize power thereby electricity bills 10–20%. Intelligent transportation systems dioxide approximately 60%. Moreover, management design cities through application further promote sustainability.

Language: Английский

---

Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

Energy and AI, Journal Year: 2022, Volume and Issue: 10, P. 100198 - 100198

Published: Aug. 8, 2022

The built environment sector is responsible for almost one-third of the world's final energy consumption. Hence, seeking plausible solutions to minimise building demands and mitigate adverse environmental impacts necessary. Artificial intelligence (AI) techniques such as machine deep learning have been increasingly successfully applied develop environment. This review provided a critical summary existing literature on methods over past decade, with special reference holistic approaches. Different AI-based employed resolve interconnected problems related heating, ventilation air conditioning (HVAC) systems enhance performances were reviewed, including forecasting management, indoor quality occupancy comfort/satisfaction prediction, detection recognition, fault diagnosis. present study explored focusing framework, methodology, performance. highlighted that selecting most suitable model solving problem could be challenging. recent explosive growth experienced by research area has led hundreds algorithms being performance-related studies. showed studies considered wide range scope/scales (from an HVAC component urban areas) time scales (minute year). makes it difficult find optimal algorithm specific task or case. also evaluation metrics, adding challenge. Further developments more guidelines are required field encourage best practices in evaluating models. while had efficiency research, still at experimental testing stage, there limited which implemented strategies actual buildings conducted post-occupancy evaluation.

Language: Английский

---

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13493 - 13493

Published: Sept. 8, 2023

Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements AI DL explores their applications achieving sustainable development goals (SDGs), renewable energy, environmental health, smart building energy management. has the to contribute 134 of 169 targets all SDGs, but rapid these technologies necessitates comprehensive regulatory oversight ensure transparency, safety, ethical standards. In sector, been effectively utilized optimizing management, fault detection, power grid stability. They also demonstrated promise enhancing waste management predictive analysis photovoltaic plants. field integration facilitated complex spatial data, improving exposure modeling disease prediction. However, challenges such as explainability transparency models, scalability high dimensionality with next-generation wireless networks, ethics privacy concerns need be addressed. Future research should focus on developing scalable algorithms for processing large datasets, exploring addressing considerations. Additionally, efficiency models is crucial use technologies. By fostering responsible innovative use, can significantly a more future.

Language: Английский

---

Analysis of Challenges and Solutions of IoT in Smart Grids Using AI and Machine Learning Techniques: A Review

Electronics, Journal Year: 2023, Volume and Issue: 12(1), P. 242 - 242

Published: Jan. 3, 2023

With the assistance of machine learning, difficult tasks can be completed entirely on their own. In a smart grid (SG), computers and mobile devices may make it easier to control interior temperature, monitor security, perform routine maintenance. The Internet Things (IoT) is used connect various components buildings. As IoT concept spreads, SGs are being integrated into larger networks. an important part because provides services that improve everyone’s lives. It has been established current life support systems safe effective at sustaining life. primary goal this research determine motivation for device installation in buildings grid. From vantage point, infrastructure supports comprise them critical. remote configuration monitoring security comfort building occupants. Sensors required operate everything from consumer electronics SGs. Network-connected should consume less energy remotely monitorable. authors’ aid development solutions based AI, IoT, Furthermore, authors investigate networking, intelligence, SG. Finally, we examine SG IoT. Several platform subject debate. first section paper discusses most common learning methods forecasting demand. then discuss how works, addition meters, which receiving real-time data. Then, SG, ML integrate using simple architecture with layers organized entities communicate one another via connections.

Language: Английский

---

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(15), P. 7882 - 7882

Published: Aug. 5, 2022

Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet Things (IoT) connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling via mobile devices computers. buildings are becoming core aspects in larger system integrations IoT is increasingly widespread. The plays an important role smart provides facilities that improve human security by using effective technology-based life-saving strategies. This review highlights buildings. platform its components highlighted this review. Furthermore, challenges regarding main factors pertaining described different methods machine combination with technologies also effectiveness make them energy efficient.

Language: Английский

---

Buildings

Cambridge University Press eBooks, Journal Year: 2023, Volume and Issue: unknown, P. 953 - 1048

Published: July 21, 2023

A summary is not available for this content so a preview has been provided. As you have access to content, full PDF via the ‘Save PDF’ action button.

Language: Английский

---

DATA SCIENCE IN ENERGY CONSUMPTION ANALYSIS: A REVIEW OF AI TECHNIQUES IN IDENTIFYING PATTERNS AND EFFICIENCY OPPORTUNITIES

Engineering Science & Technology Journal, Journal Year: 2023, Volume and Issue: 4(6), P. 357 - 380

Published: Dec. 7, 2023

This review critically examines the role of Data Science and Artificial Intelligence (AI) techniques in energy consumption analysis, focusing on their efficacy identifying patterns uncovering efficiency opportunities. The primary objective is to assess how AI methodologies are transforming with an emphasis pattern recognition optimization efficiency. study adopts a systematic literature approach, scrutinizing peer-reviewed articles published between 2015 2022. methodological framework ensures comprehensive relevant analysis current applications sector. Key findings reveal significant evolution from traditional methods sophisticated AI-driven techniques. has proven instrumental accurately predicting patterns, facilitating enhanced decision-making for management. identifies various techniques, including machine learning, deep predictive analytics, specific analysis. also delves into technological, economic, environmental implications integrating highlighting both challenges potential solutions. It underscores growing trend enhancing emerging opportunities therein. offers overview trends future directions, serving as guide industry stakeholders, policymakers, researchers harnessing more efficient sustainable analysis. Keywords: Intelligence, Efficiency Optimization, Pattern Recognition, Energy Consumption Analysis.

Language: Английский

---

Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview

Energy and Buildings, Journal Year: 2024, Volume and Issue: 305, P. 113903 - 113903

Published: Jan. 11, 2024

Language: Английский

---

How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society

Energy Policy, Journal Year: 2024, Volume and Issue: 186, P. 114010 - 114010

Published: Feb. 1, 2024

As China's energy development undergoes a process from qualitative improvements to quantitative changes, high-quality (HED) has become vital strategy of the Chinese government. representative emerging technologies, artificial intelligence (AI) can effectively promote clean transition, strengthen security, and enhance above process. Therefore, this paper explores relationship between AI HED based on gauging index level 30 provinces in China covering 2007–2017. In addition, we use green innovation R&D intensity as mediating variables study indirect effect HED. We further explore threshold digital economy The results indicate that positively affects China; specifically, every 1 % increase will lead 0.032 index. Moreover, indirectly increases by improving intensity. Further, shows influences impact This means have significantly positive areas with developed economy. Finally, provide practical approaches reference suggestions for achieve transition assistance AI.

Language: Английский

---

REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION

Engineering Science & Technology Journal, Journal Year: 2024, Volume and Issue: 5(4), P. 1243 - 1256

Published: April 10, 2024

Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control systems. This review provides a concise overview role AI in enhancing efficiency. technologies, such as machine learning neural networks, are being increasingly applied to optimize consumption various sectors, including buildings, transportation, industrial processes. These technologies analyze vast amounts data identify patterns trends, more precise systems prediction demand. One key advantages its ability adapt learn from data, leading continuous improvement energy-saving strategies. algorithms can based on factors weather conditions, occupancy patterns, prices, resulting significant cost savings environmental benefits. Furthermore, enables integration renewable sources into existing predicting generation optimizing use. helps reduce reliance fossil fuels mitigates greenhouse gas emissions, contributing sustainable future. However, implementation not without challenges. include privacy concerns, need for specialized skills develop deploy solutions, complexity integrating infrastructure. Addressing these challenges will be crucial realizing full potential optimization. In conclusion, holds great promise intelligent By leveraging organizations achieve savings, costs, contribute resilient future. Keywords: Role, AI, Energy, Efficiency, Optimization.

Language: Английский

---