###### Abstract
\begin{table}
\begin{tabular}{c c} \hline \hline
**Range** & **Duration** \\ \hline Short term load forecasting (STLF) & 1 hour to 1 week \\ Medium Term Load Forecasting (MTLF) & 1 week to 1 year \\ Long Term Load Forecasting (LTLF) & 1 year to 20 years \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ranges of different prediction horizons for load forecasting. Figure 1: Yearly Peak Demand Requirement versus Peak Demand fulfilled of India in MW. incorrect\" with more data, which these systems do by continuously updating the surrogate probability model after each evaluation of the objective function. Bayesian reasoning is a form of inductive reasoning [27]. Bayesian Optimization is designed in such a way that its current results depend upon past evaluation results, hence sufficient data are required to produce quality results [28]. A detailed analysis of all the hyperparameter tuning methods can be accessed in [29]. Instead of directly forecasting load, load is first classified using classifiers and the classified load is then fed to the regression method to predict load. This advantage of this approach is that, with classifiers, the load band is identified and regression methods are applied to that particular band instead of all the data. This improved methodology is a novel aspect of the manuscript. The following aspects have been discussed in this paper:  
* Data are completely analyzed and outliers are detected using the Inter Quartile Range (IQR) technique. After detection, outliers are removed since the data set is large;
* The cleaned data are then fed to various machine learning techniques including SVM, Ensemble, Decision Trees, Neural Networks, and GPR. The best method is proposed using statistical modeling. This statistical modeling is performed using parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), \(R^{2}\), and Prediction Speed;
* After a comparative analysis of the statistical methods, the best method is determined. This best method is further optimized by tuning parameters using three methods--Bayesian Optimization, Grid Search, and Random Search--with the objective of minimizing MSE. The method giving the least MSE is identified and proposed. A detailed flow chart of the proposed methodology is shown in Figure 2. It can be observed from Figure 2 that to select an accurate STLF model, data are first analysed and outliers are removed. Feature scaling of data is performed to normalize the independent variables of the data. After data analyses and feature scaling, data are trained using regression models such as SVM, Ensemble, Decision Trees, Neural Networks, and GPR. The best regression method is obtained by analyzing statistical parameters such as RMSE, MAE, MSE, \(R^{2}\), and Prediction Speed. The best method is further optimized with the objective of minimizing MSE by tuning hyperparameters. Figure 2: Proposed methodology for short term load forecasting. The rest of the paper is organized as follows. Section 2 explains the procedure for the data analysis of Load. Section 3 describes all the methodologies used in the paper. Section 4 deals with hyperparameter tuning. Section 5 presents the results and analysis followed by a conclusion in Section 6.