## 5 Machine Learning Algorithms
This model can act both as a classifier--categorizing air quality as poor, fair or good--and as a regressor to estimate predicted values. The GBR-SHAP model is applicable to both indoor and outdoor air quality assessments and can be used as an input to comfort estimation and load prediction models, especially when integrated with a ventilation system model. For natural ventilation prediction, a Deep Q-Network (DQN) model can be used to analyze the impact of window openings on air quality, particularly in scenarios where elevated outdoor particulate matter (PM) concentrations are expected. The use of SHAP allows users to interpret the model predictions and gain insight into the expected air quality conditions in the coming hours. In addition, the input data can be supplemented with information from local weather services to improve forecast accuracy. * Thermal loads contribute significantly to the overall energy consumption of a building. ML techniques can be used to assess the impact of external environmental conditions on total heat loss and to evaluate potential interventions to improve building insulation. For thermal load modelling, an Artificial Neural Network (ANN) combined with Local Interpretable Model-agnostic Explanations (LIME) can be used to improve the interpretability of the results [46]. In high natural ventilation scenarios, such as Near Zero Energy Buildings (NZEBs), it is possible to isolate and quantify the contribution of natural ventilation to thermal loads using an ANN model with SHapley Additive exPlanations (SHAP) [47]. Identifying the most influential factors affecting thermal loads allows the detection of critical inefficiencies that should be addressed to improve the overall energy performance of the building. The thermal load prediction model uses input data related to weather conditions, occupancy levels, natural ventilation rates, HVAC system operation, and building geometries and materials. The output of the model can then be used as input for electrical power supply models and air quality prediction models. * The aim of this model is to evaluate the indoor thermal comfort of a building. Generalized Additive Models (GAMs) have been identified as particularly suitable for thermal comfort prediction tasks [48]. K-Nearest Neighbors (KNN) and Decision Tree models can be used to generate warnings and provide recommendations to users and stakeholders. It is important to note that thermal comfort is subject to regulatory standards, as described in EN ISO 7730. Therefore, it is recommended to include empirical rules to ensure that the model keeps comfort levels within an acceptable range. The model takes data from air quality forecasting, occupancy forecasting and thermal load forecasting as input. Generated outputs include real-time alerts, activation of building electrical actuators (e.g., automated window opening), and data transfer to the HVAC control model for further optimization. * Load profiling is essential to understand the devices connected to an electrical network and to detect potential faults in the system. To address this task, a machine learning model based on a Convolutional Neural Network (CNN) has been proposed, complemented with Local Interpretable Model-Agnostic Explanations (LIME) to improve the interpretability of the CNN model [49]. The implementation of a load profiling model is also valuable for detecting fraudulent energy consumption and identifying mali cious users, as demonstrated by [PERSON] et al. [50]. In addition, accurate load profiling is a critical requirement for decision makers involved in building energy management and optimization. The model uses data input from electrical loads (e.g., HVAC systems, lifts) and external loads (e.g., electric vehicle charging stations). The generated energy profiles serve as input for subsequent load forecasting models, enabling more efficient energy management. * Load prediction is a fundamental objective of any energy monitoring system using machine learning techniques.