A Novel Approach to Optimize Energy Consumption in Industries Using IIoT and Machine Learning

Managing Electrical Energy has become crucial nowadays where everything we use works on electrical energy. Utilizing energy in an efficient and effective way can save a lot of energy. Reducing unnecessary usage of electrical current can lead to lowering carbon emission and also can directly benefit the production cost in industries. This reduction is possible when we continuously monitor the usage of the current when it's being utilized. In order to overcome this problem our novel idea proposes a state of art algorithm such as the Autoregressive Integrated Moving Average (ARIMA) model which is the link between one observation and several lagged observations of the same series modeled by the autoregressive component. In essence, it depicts how the current value is linearly dependent on its own historical values. Linear regression is a prevalent statistical technique utilized to model the association between a dependent variable and one or more independent variables. When it comes to energy consumption, linear regression can be used to examine and forecast patterns of energy use depending on a variety of influencing factors. To improve the accuracy of the proposed work, the use of Time Series Analysis aids in making the system more reliable and efficient. We have developed a system in which an all-in-one energy management solution provides a platform where we can control this wastage and manage all equipment in one place. Our proposed system monitors electrical energy using various IOT sensors to continuously monitor energy utilization and predicting the usage earlier to help the industries to make better financial plans.