## 2 Background
### Theoretical Context
#### 2.1.2 Artificial Intelligence (AI) Application  
The application of AI techniques allows extracting value from generation, transmission, distribution, and consumption data to support decision-making in power grid management,which is transforming and evolving towards an SG [7; 36; 37; 38]. AI, combined with analytics and monitoring systems, provides clear and complete visibility of the systems within the grid, allowing preventive maintenance actions, real-time incident resolution, and decision-making for operational improvements and optimizations [39; 40; 41].  
Some practical cases of the use of the application of AI in SGs can be observed in the work of [PERSON] [42], who proposes two intelligent techniques for addressing the electricity price forecasting (EPF) problem using machine learning. First, a support vector regression (SVR) model is used to predict the hourly price. Second, a deep learning (DL) model is implemented and compared with the SVR model. The results show that the two proposed models are effective tools for EPF.  
In the work of [PERSON] [43], compare tree-based ensemble machine learning models (random forest--RF and extra trees--ET), decision trees (DT) and support vector regression (SVR) to predict the useful hourly energy from a solar thermal collector system. The models developed were compared in terms of their generalizability (stability), accuracy, and computational cost.  
In a well-known case implementing an AI platform applied to the design and operation of energy-efficient buildings (EFBs) [44], [PERSON] et al. presented an adaptive ANN which can predict the unexpected behavior of incoming data and adapt to it accordingly. Two models, accumulative training and sliding window training, were tested against simulated and measured data. The sliding window technique had better performance in the case of real measurements. For simulated data, both techniques showed similar performances [45].  
[PERSON] et al. proposed a short-term building energy usage forecasting model based on an artificial neural network (ANN) model with Bayesian regularization algorithm to investigate the effects of network design parameters, such as time delay, number of hidden neurons, and training data, on the model capability and generality [46]. The model was used for day-ahead electricity usage of buildings in a 15-min resolution.  
[PERSON] and [PERSON] used a feedback ANN to predict short-term electric load consumption in buildings. The biggest advantage of this model lies in its simplicity. It used a minimal number of resources and yet its precision was comparable to other methods used for forecasting [47]. [PERSON] et al. [48] tested seven different ML techniques on different data sets, and discussed the advantages, disadvantages, and technical benefits for each technique when applied to the prediction of future hourly residential electrical consumption.  
On the other hand, in the investigation of [PERSON] et al. [49] evaluates the performance of different feature extraction or dimensionality reduction techniques for short-term energy forecasting applications using smart meter data. The number and type of input feature data are crucial for the performance of energy forecasting models. The results obtained show the importance of dimensionality reduction techniques for higher accuracy and faster training times. While linear principal component analysis (PCA) is a preferred dimensionality reduction technique for faster training times, kernel PCA, non-negative matrix factorization (NMF), independent component analysis (ICA), and uniform manifold approximation and projection (UMAP) yield better accuracies.