## 4 Machine Learning in Energy Trading  
### Machine Learning Techniques and Models  
Energy trading is a complex and dynamic process and is primarily challenging due to the need for accurate information to make precise decisions regarding energy sharing, selling, or trading [203]. These decisions depend on various criteria, such as the number of participants, energy quantities, time horizons, and pricing. Decision-making strategies can be broadly categorized into mathematical models and learning models. Within learning models, approaches can be divided into sequential decision-making tasks and data-driven tasks. Recently, data-driven approaches, particularly machine learning (ML), a subset of artificial intelligence (AI), have demonstrated promising capabilities in addressing energy trading challenges in LEMs. ML employs a range of search and optimization algorithms and statistical models to execute tasks by recognizing patterns and analyzing data sets. As a learning method, ML assists energy traders in making informed decisions based on extensive data sets and identifying patterns that may be difficult or impossible for humans to detect. A significant application of ML in energy trading is predictive modeling, where energy traders use ML to forecast energy prices, demand, and supply based on historical data and relevant information [204]. By analyzing these predictions, traders can identify opportunities to buy or sell energy at optimal prices. Additionally, ML plays a crucial role in portfolio optimization, enabling traders to enhance their energy asset portfolios by considering factors such as pricing trends, weather patterns, and production data [205] to pinpoint the most profitable investment opportunities. ML algorithms can also be used to make real-time decisions about energy trading based on incoming data from different sources, which helps energy traders respond immediately to varying market conditions and take advantage of emerging opportunities. Furthermore, ML can be applied to develop automated trading systems capable of executing trades in response to real-time market conditions and predefined rules. This feature empowers energy traders to execute quicker and more precise transactions, simultaneously mitigating the risk associated with human errors. Based on the reviewed literature, ML can be broadly classified into four distinct types: supervised, semi-supervised, unsupervised, and RL. Figure 8 categorizes these ML models as they relate to their application within the context of the LEM. \"Learning with a teacher\" is the best representation of supervised learning, in which the model learns from a set of training data provided by a specific internal or external knowledgeable teacher in a supervised form [206]. The training data are based on both the applied input labels and desired outputs, which can effectively map and predict the updated output associated with the corresponding inputs. The model weights are updated iteratively to minimize the mean variation in predictions and labels. Regression and classification are the two major variants of supervised learning to predict the value of the dependent attribute from the attribute variables [207]. In the regression approach, the dependent attribute is numerical, and continuous output is returned, while for classification, the dependent attribute is categorical, and a discrete value is returned. \"Learning without a teacher\" is the true reflection of an unsupervised learning approach, where patterns are inferred from unlabeled data, and the output data are presented without any inputs; this relates to a self-organization approach to capturing unknown data patterns. Association and clustering are the two major variants of unsupervised learning which try to connect, associate, and cluster the unlabeled data pattern together based on similarities [208]. Both supervised and unsupervised learning have limitations in terms of insufficient labeled data and unpredicted clustering, respectively.