# Reducing Emissions Using Artificial Intelligence in the Energy Sector: A Scoping Review
## 3 Results
### AI Applications in Energy Efficiency for Emission Reduction  
A third notable trend in leveraging AI for emission reduction within the energy sector is the optimization of building energy consumption. According to the European Environment Agency indicator, building energy usage represents 34% of energy-related greenhouse gas emissions in the EU in 2022 [60]. Moreover, [PERSON] et al. [7] estimated that buildings contributed 6% of global CO2 emissions in 2018. By targeting the energy efficiency measures in the building sector, significant gains in reducing overall CO2 emissions could be achieved. [PERSON] et al. [61] studied building energy efficiency in the public sector and tested machine learning models to predict the specific energy consumption (SEC) of the buildings. SEC expresses the energy consumed per m\({}^{2}\) while taking into consideration external influences, such as the heating season length, thus enabling the comparison of different buildings in different climates [62]. The dataset for this research was sourced from the Croatian Energy Management Information System. The authors evaluated deep neural networks, CART decision trees, and random forests for predicting the building SEC. The authors discovered that random forest was the best performing model for the task. The authors suggested that the models can be used to predict the energy consumption of new buildings, thus aiding planning and therefore reducing future energy usage and emissions. [PERSON] et al. [63] trained artificial neural networks to predict building retrofit solutions with the focus on reducing the building CO2 emissions while considering the optimal cost. The retrofit solutions included considerations for heating, building insulation, renewable installation, and energy storage. The idea of this study was to replace an existing complex modeling software with a lighter and faster neural network model that achieves the same accuracy with reduced computational requirements and a reduced number of required input variables that are hard to obtain. The authors conducted a case study for buildings in Zurich, Switzerland by collecting information about buildings in this area from multiple different data sources and combining that information to urban building energy modeling data to train the neural networks. The proposed method predicted ten solutions from the Pareto front of multi objective solutions of reducing CO2 emissions with optimal cost. The authors suggested that the simpler and computationally lighter model accelerates the adoption of CO2 emission-friendly retrofit solutions, and therefore contributes to the reduction in CO2 emissions. [PERSON] et al. [64] presented a global model for reducing carbon emissions in residences. The study demonstrated how IoT and AI could be used globally to reduce carbon emissions caused by the energy consumption of cities. The authors envisioned a global model where data are gathered from IoT devices, and AI can intervene in device energy usage with minimal interference to the residents. The proposed system is constructed from multiple levels, the highest of which is the global unit. The global unit contains multiple country units, which in turn contain multiple city units, which in turn contain multiple residence units. The proposed topology focuses on minimizing data from the IoT devices that are connected to the centralized and hierarchical AI units. The authors suggested that the proposed topology minimizes the computational resources required for transferring and processing the data between the hierarchical levels. Otherwise, the amount of data gathered from billions of devices would require a significant amount of computing power. The authors tested a Decision Tree model in simulated setting and discovered that the visioned system is capable of reducing the carbon emissions up to 21%. [PERSON] and [PERSON] [65] developed a machine learning classifier to predict building energy conservation measures (ECMs) using energy audit data from over 1100 buildings.