An Analysis Of Energy Demand In Iot Integrated Smart Grid Based On Time And Sector Using Machine Learning

Abstract

Smart Grids (SG) encompass the utiliza- tion of large-scale data, advanced communication in- frastructure, and enhanced efficiency in the manage- ment of electricity demand, distribution, and produc- tivity through the application of machine learning tech- niques. The utilization of machine learning facilitates the creation and implementation of proactive and au- tomated decision-making methods for smart grids. In this paper, we provide an experimental study to un- derstand the power demands of consumers (domestic and commercial) in SGs. The power demand source is considered a smart plug reading dataset. This dataset is large dataset and consists of more than 850 user plug readings. From the dataset, we have extracted two different user data. Additionally, their hourly, daily, weekly, and monthly power demand is analysed individ- ually. Next, these power demand patterns are utilized as a time series problem and the data is transformed into 5 neighbour problems to predict the next hour, day, week, and month power demand. To learn from the transformed data, Artificial Neural Network (ANN) and Linear Regression (LR) ML algorithms are used. According to the conducted experiments, we found that ANN provides more accurate prediction than LR Addi- tionally, we observe that the prediction of hourly de- mand is more accurate than the prediction of daily, weekly, and monthly demand. Additionally, the pre- diction of each kind of pattern needs an individually refined model for performing with better accuracy.