# Data-Driven Decision Support for Smart and Efficient Building Energy Retrofits: A Review
## 1 Introduction  
Buildings in the European Union (EU) consume nearly 40% of the total energy [1] (in 2020), significantly contributing to energy demand and associated emissions (European Commission, 2018). Building operations account for 30% of global final energy consumption and 26% of global energy-related emissions (8% being direct emissions in buildings and 18% indirect emissions from the production of electricity and heat used in buildings). Direct emissions from the buildings sector decreased in 2022 compared to the year before, despite extreme temperatures driving up heating-related emissions in certain regions. In 2022, energy use in the building sector increased by around 1% [2]. Given the substantial impact of the sector, there is considerable potential for cost-effective enhancements in energy efficiency and reductions in greenhouse gas emissions. To meet the EU's ambitious energy and environmental targets for 2030 and 2050, there is a strong focus on improving the energy performance of buildings through legislative measures like the Energy Performance of Buildings Directive [3]. This directive seeks to accelerate the renovation of buildings, particularly those with the poorest performance, enhance air quality, promote the digitization of energy systems, and support the development of infrastructure for sustainable mobility. Despite these efforts, global progress in implementing energy efficiency measures in existing buildings remains insufficient, falling short of the actions needed to reach net-zero carbon dioxide emissions by 2050. Regarding existing buildings, making upover 97% of the building stock, 75% are considered energy inefficient, heavily dependent on fossil fuels, and equipped with outdated technologies and appliances [4]. In the EU, over 220 million building units exist, with 85% constructed before 2001, and it is projected that 85-95% of these buildings will still be in use by 2050 [5]. Globally, improving the energy performance of buildings is critical to meeting the United Nations' Sustainable Development Goals (SDGs) for 2030. The building sector, as the largest energy consumer worldwide, accounts for 37% of the final energy consumption (UN, 2022) [6]. In 2022, buildings consumed 132 exajoules (EJ) of energy. This sector's significant contribution to global warming is evident. From 2010 to 2022, energy consumption in buildings increased from 119 EJ to 132 EJ, driven by improved energy access in developing countries and the growing use of energy-intensive devices. To meet the Paris Agreement's emission reduction targets [7], the building sector must adopt more effective retrofit strategies. The International Energy Agency (IEA) projects that significant declines in global energy demand in buildings can be achieved through energy efficiency improvements, aiming for a 40% reduction by 2040 [8]. This includes the deployment of more efficient heating systems, better insulation, and the increased use of renewable energy sources. The 'Low Energy Demand' scenario by [PERSON] et al. [9] targets limiting global warming to 1.5 \({}^{\circ}\)C by 2050, which requires doubling the current retrofit rate and adopting advanced thermal end-use technologies. In this context, data-driven decision support systems are emerging as a pivotal component in enhancing building energy retrofits. Leveraging advanced analytics, machine learning algorithms, and big data, these systems provide accurate predictions, optimize energy use, and suggest the most cost-effective and impactful retrofit measures. Current methods for building energy modeling can be categorized into white-box, gray-box, and black-box models. White-box models, based on building physics principles, require detailed inputs and are often cumbersome. Gray-box models combine physics-based approaches with in situ measurements, while black-box models rely entirely on measured data and use statistical and machine learning techniques.