The literature on the study of load around the world shows that understanding these relations with influential factors plays a major role in clarifying the demand fluctuation and helps develop better load forecasting models for electric load [5]. Short-term load forecasting (STLF) has become an important subject to energy suppliers and system operators for efficiently operating electric power systems. A robust STLF model allows for tracking the hourly load variation of load for the day or even week ahead with high accuracy. In developed countries, electricity distribution is fixed to a day ahead, which in real time is managed using demand-side management strategies, resulting in the reduction of high operation costs. However, a lack of accuracy in load forecasting can have a huge impact on system reliability [6]. The complex behavior and fluctuating nature of electricity demand is a major challenge when developing a robust model. Fluctuation in the consumption of electricity is due to the random behaviors of the consumers, but within all the randomness, a few predictable patterns can be separated, such as seasonal, weekly and daily variation [7]. As human behaviors and requirements change with various factors such as the time of day or the day of the week, holidays tend to follow similar demand patterns, which make them easier to track [8]. These deterministic variables show a major impact on the electricity demand, especially during special days. As such events result in people traveling, most people travel away from major cities largely during long holidays or religious holidays. Local festivals and major events such as the new year have a huge impact on the load consumed [7]. This effect can be observed in various locations, which affects the consumption pattern [9; 10]. Understanding these special days can add to improving the forecasting accuracy of the load. For short-term load forecasting, the hourly influence has a significant effect on the load such that variations in consumption during the morning hours, day time, and night hours show a daily demand pattern of electricity. Likewise, stochastic variables such as environmental factors and human behaviors toward the change in the environment also provide information on changing electricity demand. Among all factors, the outdoor temperature shows an important influence that explains the variation in demand [7; 11; 12]. The influence can be linked to the use of electric cooling and heating appliances used within the households. The literature shows that a change in the outdoor temperature results in an increase or decrease in the use of appliances, resulting in a change in electricity demand. As multiple examples of research show that heating and cooling appliances are responsible for more than half the energy consumption in the residential load [7], implementing the impact of temperature as an input to the forecasting model can improve the accuracy of the load forecasting model. Being the most populated city in Nepal, Baneshwor is the center of the capital city of Nepal, comprising major human activity and an official workstation. As a developingcountry, centralization in the city is a major issue, which results in increasing demand. In such conditions, keeping the supply intact within the valley becomes important to maintain day-to-day activities. To avoid any power surges or loss, a proper study of the load should be performed. Forecasting the load for each hour of the day allows the Nepal Electricity Authority to avoid any major changes to the system which may cause a blackout, load shedding, or voltage fluctuation in the system. Therefore, the main objective of this paper is to present an accurate short-term load forecasting model and perform an evaluation of the impact of electric load influencing factors that affect the consumption of electricity. Based on the literature, this paper is the first to present a detailed study of the variables affecting electricity load consumption within Nepal.