## 1 Introduction  
The growing decentralization and digitization of the power industry is driving the relevance of artificial intelligence and data analysis in particular. Today's data science studies include a wide range of topics along the value chain, from generation and trade through transmission, distribution, and consumption. Diverse applications and different methodologies, such as various types of artificial neural networks, have also been studied. From the applications of data analysis, it can be used in four ways:  
* Forecasting and Prediction. * Monitoring and Controlling. * Clustering. * Others [1]. Any industrial growth story includes the thermal power industry. However, it now functions in a complicated context characterized by significant unpredictability in operational circumstances such as grid changes, a competitive market (alternative sources), fuel input, regulatory constraints, and changing staff. This creates a difficult operating environment and reduces plant profitability. [PERSON] [2] proposed that with a greater rate of growth, more maintenance is required, and a complete availability analysis will aid in identifying components that are primarily responsible for low availability. It can be useful in the installation of future power plants. It is critical for the efficient and economic operation of a power plant to properly anticipate the full load electrical power output of a base load power plant. It is useful for getting the most money out of the available resources. In contrast to earlier research publications that explored data analytics in power plants and their components, [PERSON] [3] made a comparison study on multiple machine learning (ML) algorithms for predicting combined cycle power plant (CCPP) full load electrical power outputs. The prediction was done for the same using the Genetic Algorithm by [PERSON] [4]. When the supplied findings were examined, artificial neural networks have much greater Root Mean Squared Error (RMSE) than other approaches, including basic regression functions. For a proper system analysis utilizing thermodynamical approaches, a significant number of assumptions are necessary, such that these assumptions account for the unpredictability of the outcome. Without these assumptions, a thermo-dynamical analysis of a real-world application would demand hundreds of nonlinear equations, the solution to which would either be impossible or would take too much time and effort. So, [PERSON] [5] highlighted that machine learning algorithms are increasingly being used as an alternative to thermo-dynamical approaches to analyze systems with unpredictable input and output patterns to overcome this barrier. As a result, when considering a machine learning approach for predicting output in a power plant, various algorithms or methods can be used, but selecting the most efficient and appropriate method is critical. In [6], a hybrid Machine Learning algorithm for the prediction of landslide displacement was proposed. It was implemented for Shuping and Baishuie landslides to calculate the hyperparameters. Ref. [7] demonstrates a support Vector Machine for the prediction of seepage-driven landslides. As power plant equipment is complicated and difficult to predict precisely, [PERSON] [8] proposed an artificial neural network for condition prediction that was developed using principal component analysis (PCA). Author [PERSON] [9] demonstrated a viable prediction model of power for grid-connected solar plants based on artificial neural networks. This neural model gives the hourly prediction value of power in steps, using the input vector as well as the past power value and other impact elements. In [10], [PERSON] proposed a new network model which consists of Elman Neural Networks (ENN) and principal component analysis (PCA). This prediction model helped to improve the wind power usage while also developing the stability and safety of the plant.