# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 5 AI and Machine Learning Algorithms in Energy Retrofitting  
It is very well demonstrated that energy retrofitting plays a crucial role in improving the energy efficiency of existing buildings, which is essential for reducing overall energy consumption and minimizing greenhouse gas emissions. Traditionally, retrofitting ap  
Figure 3: Categorization of datasets used in reviewed studies. proaches have relied on empirical guidelines and generalized methods, which may not always consider the unique attributes of each building or provide optimal results. The advent of artificial intelligence (AI) and machine learning (ML) represents a transformative shift in this field. AI encompasses the development of systems that perform tasks requiring human-like intelligence, such as problem-solving, decision-making, and learning. Within AI, machine learning (ML) is a subset that focuses on creating algorithms capable of learning from data and making predictions or decisions without explicit programming for each specific task. ML algorithms can be categorized into supervised learning, which involves training models on labeled data to predict outcomes; unsupervised learning, which identifies patterns in unlabeled data; reinforcement learning, which learns optimal strategies through interactions and feedback; and deep learning, which uses complex neural networks to model intricate data patterns. By leveraging these advanced technologies, AI and ML enable more precise, data-driven approaches to energy retrofitting. They offer significant advantages over traditional methods, such as enhanced accuracy in predictions, real-time monitoring, and dynamic adjustments, ultimately leading to more effective and efficient energy upgrades in buildings. Types of Artificial Intelligence and Machine Learning Algorithms in Building Retrofitting  
The rapid evolution of AI and ML technologies has introduced a diverse range of algorithms, each uniquely suited to different aspects of energy retrofitting. The application of these algorithms in the built environment enables more precise energy consumption modeling, the accurate prediction of retrofit outcomes, and the effective optimization of energy conservation measures. Understanding the variety and capabilities of these algorithms is essential for leveraging their full potential in energy retrofitting. Each type of AI and ML algorithm presents distinct advantages and trade-offs, making them more or less suitable for specific retrofitting tasks. The choice of algorithm depends on various factors, including the nature of the data, the complexity of the retrofitting problem, and the specific objectives of the project. The subsequent sections delve into the most widely used AI and ML algorithms in energy retrofitting, emphasizing their distinctive contributions and specific applications within this domain. Supervised Learning:Algorithms such as Linear Regression, Decision Trees, and Support Vector Machines (SVMs) are frequently employed to predict energy consumption and evaluate the impact of retrofitting measures. These models require labeled datasets to learn the relationships between input features and output predictions. The authors in [PERSON] et al. [26] use LSTM to understand the relationship between simulated and metered energy consumption and improve prediction accuracy. LSTMs bring significant advantages to energy retrofitting projects, especially in scenarios involving time-series data and complex, dynamic systems. Their ability to learn from past data and predict future trends makes them a powerful tool for optimizing energy efficiency in buildings, ensuring that retrofitting efforts are both effective and sustainable in the long term. [PERSON] et al.