An analysis of energy demand in iot- integrated smart grid based on time and sector using machine learning

Anotace: Smart Grids (SG) involve big data, communication infrastructure, and improved productivity, power need and it’s distribution management with machine learning. Machine learning enables us to design and develop proactive and automated decision-making techniques for SG. In this paper, we provide an experimental study to understand 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 individually. 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. In order 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. By using proper parameter tuning can also improve the more prediction ability of ANN thus in near future we propose to tune the network for getting better prediction performance. Additionally, we observe that the prediction of hourly demand is more accurate than the prediction of daily, weekly, and monthly demand. Additionally, the prediction of each kind of pattern needs an individually refined model for performing with better accuracy.