Electric Power Load Forecasting (EPLF) refers to the prediction of electric power consumption in electric power systems. It is an essential process in the planning of future electric power demand and operation of such systems. Accurate and efficient forecasts can lead to substantial savings in operating and maintenance costs, better reliability of power supply and delivery system. This study aims to implement long term forecasts which are usually carried out in a period ranging from a month to a few years, at various sections of TIET, Patiala. Historic electric meter readings will be used to perform training and testing analysis on regression models and other algorithms, which will further assist in predicting future power requirements. Python3 is employed to implement the above model as a user friendly, GUI based software to assist power administrators. The system is protected by facial recognition features implemented through Computer Vision using Raspberry Pi. This would help us to visualize the changing trends of electricity consumption which our campus faces. Proposed work will be compared with existing techniques to prove the efficacy.