# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
Neural networks learn complex patterns and relationships within data by adjusting the weights of connections between neurons through a process known as training, which enables the network to minimize a loss function. Neuro-fuzzy systems [175] are hybrid computational models that capitalize on the advantages of both neural networks and fuzzy logic. These systems combine the interpretability and reasoning capabilities of fuzzy logic with the learning capabilities of neural networks, as shown in Figure 11b. A neural network structure that maps inputs to outputs is the typical component of a neuro-fuzzy system. The nodes represent fuzzy sets, and the connections represent rules. The fuzzy sets enable the system to manage imprecise and uncertain information by designating degrees of membership. The neural network then modifies these membership functions and rules through a learning process. This combination allows neuro-fuzzy systems to model complex, nonlinear rela  
Figure 11: AI models of (**a**) Neural Networks, (**b**) Neuro-Fuzzy Logic [173], (**c**) RL, (**d**) BiLSTM, (**e**) LSTM, and (**f**) Decision Trees with Random Forest, where \(Y_{t}\), \(X_{t}\), \(h_{T}\), and \(h_{0}\) are the parameters of output, input, and hidden states at the index \(t\) and the initial hidden state, respectively. For the Neuro-Fuzzy, [\(A_{i}\), \(B_{i}\)] is the fuzzy set, \(W_{i}\) considered the network weights, as well as [\(x\), \(y\)] and \(f\) are the inputs and outputs, respectively. For the Decision Tree and Random Forest, \(DN\) and \(LF\) are the decision nodes and leaf nodes, respectively. tionships and provide human-readable rules. Their effective applications necessitate both adaptability and interpretability, such as control systems, decision-making, and pattern recognition. Regarding LSTM [176] and BiLSTM [177] (Figure 11d,e), the LSTM architecture is composed of memory cells and gating mechanisms including input, output, and neglect gates, which regulate the flow of information. This enables the network to retain pertinent information over extended periods of time. LSTMs are notably effective for tasks such as speech recognition, natural language processing, and time series forecasting due to this structure. By processing data in both forward and backward orientations, BiLSTM networks extend the LSTM architecture, thereby capturing context from both past and future states of the system. This bidirectional approach improves the model's capacity to comprehend the context and dependencies in sequential data. As shown in Figure 11f, decision trees [178] are a form of supervised machine learning model that is employed for regression and classification tasks. They function by recursively partitioning the input data into subsets based on feature values, thereby establishing a tree-like structure of decision nodes and leaf nodes. The predicted outcome is represented by the leaf nodes, while each decision node represents a feature and a threshold. Decision trees are intuitive and accessible due to the tree structure, which enables the simple interpretation and visualization of decision rules. Nevertheless, they are not immune to overfitting, particularly when dealing with intricate datasets. Conversely, random forests are an ensemble learning approach that enhances the robustness and precision of decision trees. A random forest [179] is composed of a series of decision trees, each of which is trained on a random subset of the training data and a random subset of features. The final output is determined by aggregating the results, typically through majority voting for classification or averaging for regression after each tree independently classifies or regresses the input during the prediction period. This ensemble approach improves the model's generalization capabilities and performance on unseen data by reducing overfitting and variance. Random forests are extensively employed in a variety of sectors, such as finance, healthcare, and marketing, as a result of their adaptability and precision.