PREDICTING ENERGY CONSUMPTION USING MACHINE LEARNING METHODS

Abstract

Electricity is presently the foremost vital energy vector within the domestic sector and business. In contrast to fuels, electricity is difficult and pricy to store. This creates the necessity of precise coupling between generation and demand. Additionally, the transmission lines of electrical power have to be compelled to be sized for a given most power, and overloading them might end in blackout or electrical accidents. For these reasons, energy consumption prognostication is important. The power to predict future energy consumption is incredibly vital for energy distribution corporations as a result of it permits them to estimate energy wants and provide them consequently. Consumption prediction makes it attainable for those corporations to optimize their processes by, for instance, providing them with data concerning future periods of high energy demand or by sanctioning them to adapt their tariffs to client consumption. This text reviews a number of the most machine learning models capable of predicting energy consumption, in our case study we have a tendency to use a particular set of knowledge extracted from a two-year-period of a college buildings.