## 2 Literature Review  
Electricity load forecasting is a prediction evaluation technique that is conducted mostly by the utility power company to find the tentative demand for energy for the future. It allows the utility company to understand load growth and fluctuation, which helps the suppliers manage their generation according to the demand of the end users. Forecasting has become an important process in the planning and operation phases of the system, allowing energy companies to have a better understanding of the demand characteristics. Load forecasting is conducted at different levels, such as the long term, medium term, and short term [5]. Depending on the requirements, forecasting is conducted for the long term, covering yearly load demand prediction, the medium term forecasts the load between one week and months, and short-term forecasting ranges between one hour and one week. However, demand forecasting is sensitive to various aspects, ranging from previous load consumption to socioeconomic factors, as well as changes in weather conditions. Variable selection for performing the forecast is a significant part of the process. In terms of the long-term forecasting of load for a country, the forecast considers the domestic production, population change, import-export trade, overall weather, and economic change observed in that country as major variables [13]. A study performed for long-term energy consumption forecasting included the gross domestic production (GDP), population, import-export trade, and primary energy consumed per year and forecasted the consumption pattern [13]. Likewise, medium-term load forecasting is applied to identify maintenance scheduling, coordinate supply stations, manage energy units, and develop cost-effective energy purchase strategies [14], whereas short-term forecasting focuses on identifying the hourly variation of demand such that the study observes the day-to-day energy trends, seasonal effects, temperature variations throughout the day, and day-to-day consumer activities [7; 8; 10]. Model selection is another important part of the process for the better forecasting of load. To maintain accuracy, various approaches have been in practice in forecasting studies, such as the multiple linear regression model [8], autoregressive integrated moving average model (ARIMA) [15], exponential smoothing [16], deep learning including artificial neural networks [17], recurrent neural networks, particle swarm optimization (PSO), and hybrid techniques [5]. Among the various models being used, deep learning has gained much attention recently [7; 15; 17]. This approach of the machine learning model breaks through the constraints of the original mathematical calculations in terms of accuracy. It realizes and identifies the pattern within the data through neural networking and is better at tracking the nonlinearly within the data [13]. However, training the model with proper parameters and constraints is the challenging part of neural network modeling. For better tracking, the patterns within the data hybrid model combined with the neural network hold the upper hand, as the comprehensive learning particle swarm optimization (CLPSO)-based memetic algorithm (CLPSO-MA) shows evolved feature selection and parameter optimization simultaneously [18]. Similarly, the authors of [19] implemented particle swarm optimization as a training algorithm to adjust the weights of artificial neural networks, where the particle swarm algorithm has shown its ability to minimize the error function with maximum efficiency. These models of forecasting demand great computation and programming skills, such that data are passed through a black box for processing. However, the conventional approach [8] of prediction that uses a mathematical approach presents a satisfying level of accuracy. The study classified the hourly load data through the statistical approach for identification of an optimal mode of prediction.