A Novel Recommended Approach for Optimized Smart Electricity using Deep Learning

Energy Usage and Optimization has been one of the major factor that requires various automation and optimized solution for the problem that we are facing in the time period. The importance of regulating the usage of energy with reduced power wastage throughout the chain of supply is one of the cardinal issues that many wide-scale sectors face today. Transmission of power plays a crucial role in the supply chain, there have been various efforts that were put forth in order to provide a much optimized solution, but the problem of power inconsistency has been an consistent one since the invention and wide scale usage of it was implemented. There has been a wide study conducted all around the world regarding the power loss that has been incurred by us while the transmission of power and its usage and it has been aggregated to around 8.25% all over the world. So there is a crucial need to find a much more efficient solution in order to mitigate the problem and in order to save power and use it simultaneously in a much sophisticated way. After completing a wide range of study over the underlying pattern in the usage of energy in several households we have come up with a solution that will predict and provide a much accurate recommendation regarding the usage of electrical energy by the particular sector or household and will bolster us with accurate findings from the data that will be used by the system regarding the usage of the power by that particular demographic region and will provide a much better solution regarding how the power wastage and usage can be minimized by reducing unwanted usage and leakage respectively by using deep learning and an ensemble of statistical and machine learning algorithms and methods. Thus, this will help the end user to reduce the usage of energy thereby providing him with solutions and future findings regarding the general pattern of usage of energy by the household, thereby aiding him in reducing the cost significantly.