A Meta-Survey on Intelligent Energy-Efficient Buildings
Abstract
:1. Introduction
2. A Brief Background Discussion
2.1. Sensors in IEEBs
- Temperature sensors, which can measure the temperature levels in IEEBs;
- Humidity sensors, that track the moisture level in the air;
- Occupancy sensors, which help in detecting the presence of people in IEEBs;
- Light sensors, which measure the intensity of light in IEEBs;
- Switch contact sensors, which detect if windows/doors are opened in an IEEB;
- CO2 and air quality sensors, which monitor the air quality in an IEEB.
- Smart meters, which measure electricity, water, and gas consumption in an IEEB.
- Smoke and fire sensors, which are used for security purposes in IEEBs.
2.2. Overview of Supervised, Unsupervised, Semi-Supervised, and Self-Supervised Learning
2.3. Description of Specific ML Algorithms Used in IEEBs
2.4. Description of Specific DL Algorithms Used in IEEBs
2.5. Description of Reinforcement Learning Algorithms Used in IEEBs
2.6. Federated and Transfer Learning
3. Survey Strategies: Intelligent Energy-Efficient Buildings
3.1. The Goal of the Investigation
- RQ1
- How is the field related to IEEBs specified or defined?
- RQ2
- What architectures are most commonly used for IEEBs?
- RQ3
- Which ML methods are most commonly used in IEEBs?
- RQ4
- What sort of dataset or real implementation is utilized to realize IEEBs?
- RQ5
- What are the main challenges and research directions in the field of IEEB?
3.2. Inquiry Search Techniques
3.3. Eligibility Criteria
- The term “ML” or one of its equivalents is either misused or poorly defined.
- The work has already been extended or is a pre-print.
3.4. Survey Preference
4. Literature Review: Intelligent Energy-Efficient Buildings
4.1. Overview of the Selected Surveys
4.2. Research Question 1
4.3. Research Question 2
4.4. Research Question 3
4.5. Research Question 4
4.6. Research Question 5
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Title | Year | Citations * | Technology Used in the Reviewed Papers | Prisma | Objective of the Review | Use Cases Highlighted | Comments/Remark |
---|---|---|---|---|---|---|---|
1. Machine Learning for Smart and Energy-Efficient Buildings [3] | 2022 | 10 | ML | No | Comparison of several works involving IEEBs for maintaining occupant comfort, health, and safety. | Smart Home | Several experiments are compared and discussed, but no discussion is presented on the data security during model training. |
2. Machine learning methods in smart lighting towards achieving user comfort [8] | 2022 | 44 | ML | No | Comparison of the ML algorithms used to control the lighting in buildings. | Smart Home | This paper reviews smart lighting applications in IEEBs. However, focusing only on smart lighting can be limiting. |
3. A review on the current usage of machine learning tools for daylighting design and control [5] | 2022 | 19 | ML (Supervised) | No | Comparison of works optimizing daylighting for reaching Energy Efficiency. | Smart Building | The paper exclusively focuses on ML (particularly ANNs) in daylighting optimization. |
4. The role of machine learning and the internet of things in smart buildings for energy efficiency [4] | 2022 | 29 | ML | No | Exploring ML methods with IoT technologies to enhance the effectiveness of smart buildings for energy efficiency. | Smart Building | The paper discusses various perspectives on IEEB and IoT. However, the paper did not specifically mention which ML algorithms technologies are most effective for IEEBs. |
5. A review of deep reinforcement learning for smart building energy management [9] | 2021 | 141 | DRL | No | Comparison of papers that used DRL for energy management. | Various Buildings | The work reviews DRL algorithms applied to energy management in Smart Buildings. However, the review misses papers introducing long learning time for DRL agents. |
6. A review of reinforcement learning for autonomous building energy management [10] | 2019 | 169 | RL | No | Analysis of works that employed RL for energy management. | Various Buildings | The paper mainly reviews studies on simulated building energy management with RL, emphasizing the need for precise simulators and realistic data. |
7. Reinforcement learning in sustainable energy and electric systems: a survey [11] | 2020 | 148 | RL | Yes | Overview of RL in sustainable energy systems, and discussion of applications, future challenges, and opportunities. | Smart Building | The paper acknowledges the potential of RL in sustainable energy. The paper also highlights its limited practical implementation. |
8. Machine learning and deep learning in energy systems: A review [12] | 2022 | 80 | ML/DL | No | Review of work on energy efficiency, management and analysis. | Smart Buildings | The paper studies the methods and applications of ML and DL in energy systems. However, the authors do not cover how these systems work when things are uncertain (outcomes or conditions are not known or predictable). |
9. A Review on Deep Learning Techniques for IoT Data [13] | 2022 | 87 | DL | Yes | Analysis of articles in which DL is used for IoT data processing. | Various Buildings | The article provides valuable perspectives on utilizing DL for handling IoT data, but it does not discuss the research limitations. |
10. Federated learning for smart cities: A comprehensive survey [14] | 2023 | 54 | ML/DL (FL) | No | Analysis of works using ML, DL, and FL in Smart Cities applications. | Smart Cities and Buildings | The paper reviews works regarding FL integration with smart city/grid/healthcare/ governance/disaster management/industries monitoring, that also consider energy efficiency. It lacks a detailed discussion on FL challenges in smart cities, including scalability, data complexity, and heterogeneity. |
11. A Review of Federated Learning in Energy Systems [15] | 2021 | 16 | ML/DL (FL) | No | Review of works using ML, DL, and FL in various fields for energy efficiency. | Various Buildings | This article discusses energy demand response, identification, prediction, and optimizations. However, the authors do not discuss potential drawbacks or risks associated with FL in energy systems. |
12. Forecasting energy use in buildings using artificial neural networks: A review [7] | 2019 | 199 | ANN | No | Analysis of works about the forecasting of energy spent in buildings by using ANNs. | Smart Building | The paper highlights the importance of ANNs in energy forecasting. However, it acknowledges the limitations of the works in literature in adapting to seasonal variations and extrapolating beyond the range of the trained data. |
13. Application of machine learning in thermal comfort studies: A review of methods, performance and challenges [16] | 2022 | 64 | ML | No | Review and comparison of works focused on energy efficiency and thermal comfort. | Smart Building | In the IEEB domain, authors considered thermal comfort and energy efficiency. However, the authors do not provide a comprehensive comparison of different ML algorithms. |
14. Review of occupant-centric thermal comfort sensing, predicting and controlling [6] | 2020 | 97 | ML | No | Review and comparison of works focused on energy efficiency and thermal comfort. | Smart Building | The research investigates data-driven approaches to enhance thermal comfort and energy efficiency. Yet, this study does not provide in-depth information on control strategies and the efficacy of non-invasive sensors. |
15. A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment [17] | 2019 | 117 | AI, ML and DL | No | Review on AI and Big data applications for IEEBs. | Smart Building | This work analyzes AI techniques to design energy-efficient buildings. However, this paper suggests exploring data mining and optimal weather data for better energy efficiency. |
16. An overview of machine learning applications for smart buildings [18] | 2021 | 141 | ML | No | Focuses on energy management in IEEBs. | Smart Building | The paper provides an overview of IEEBs focusing on the learning ability from a system-level perspective. There is a lack of discussion on implementing autonomous AI agents and training environments in real-world building scenarios. |
17. Artificial intelligence evolution in smart buildings for energy efficiency [19] | 2021 | 79 | AI and ML | No | AI technologies applied to IEEBs with a focus on Building Management Systems and demand response programs. | Smart Building | The study explores the latest advancements in AI-based modeling techniques for predicting building energy consumption, encompassing various areas like energy efficiency, occupant comfort, architectural design, and facility upkeep. However, it does not delve into aspects such as security, analysis of occupant behavior, or predictive maintenance. |
18. Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review [21] | 2024 | 0 | ML and DL | No | Review of forecasting methodologies for energy consumption in smart buildings. | Smart Building | This study examines the dataset, types of load, prediction, precision, and assessment criteria in IEEBs. For additional exploration, hybrid models that merge various DL structures could enhance accuracy with extensive datasets. |
19. Digital twin technology for thermal comfort and energy efficiency in buildings: A state-of-the-art and future directions [20] | 2023 | 07 | ML and Digital Twins | No | Review of digital twin technology for thermal comfort and energy management in smart buildings by using DL. | Smart Building | Digital twins can help occupants, increase human-centered solutions, and boost energy prediction levels. However, the paper does not cover the challenges and benefits of digital twins in IEEBs that take care of thermal comfort. |
20. A review of building digital twins to improve energy efficiency in the building operational stage [22] | 2024 | 0 | ML and Digital Twins | No | It focuses on digital twins for energy efficiency operations in buildings. | Various Building | The paper examines the use of digital twin technology in IEEBs for building operations. It outlines five primary applications, which include monitoring components, detecting anomalies, and optimizing operations. However, a significant obstacle lies in effectively integrating data acquisition systems with Building Management Systems. |
21. Machine Learning Applications for Smart Building Energy Utilization: A Survey [23] | 2024 | 0 | ML | No | Systematic review of applications and ML methods for optimizing energy utilization. | Smart Building | The paper offers a unique classification system (taxonomy) for energy applications in IEEBs. It proposes further research in decentralized, diverse real building structures, but it does not discuss the potential challenges involved in this endeavor. |
Architecture | Pros | Cons |
---|---|---|
Edge | Minimal latency, real-time control | Limited computational resources |
Fog | Localized handling, reduced latency | Limited scalability compared to cloud computing |
Cloud | Scalable, robust computing resources | High latency, data transfer expenses |
Hybrid | Low latency for latency-sensitive tasks, access to cloud resources | Needs more complex management and orchestration |
Distributed | Scalable, high performance | Can be complex to maintain and deploy |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Islam, M.B.; Guerrieri, A.; Gravina, R.; Fortino, G. A Meta-Survey on Intelligent Energy-Efficient Buildings. Big Data Cogn. Comput. 2024, 8, 83. https://doi.org/10.3390/bdcc8080083
Islam MB, Guerrieri A, Gravina R, Fortino G. A Meta-Survey on Intelligent Energy-Efficient Buildings. Big Data and Cognitive Computing. 2024; 8(8):83. https://doi.org/10.3390/bdcc8080083
Chicago/Turabian StyleIslam, Md Babul, Antonio Guerrieri, Raffaele Gravina, and Giancarlo Fortino. 2024. "A Meta-Survey on Intelligent Energy-Efficient Buildings" Big Data and Cognitive Computing 8, no. 8: 83. https://doi.org/10.3390/bdcc8080083
APA StyleIslam, M. B., Guerrieri, A., Gravina, R., & Fortino, G. (2024). A Meta-Survey on Intelligent Energy-Efficient Buildings. Big Data and Cognitive Computing, 8(8), 83. https://doi.org/10.3390/bdcc8080083