Prediction of domestic power peak demand and consumption using supervised machine learning with smart meter dataset

The prediction of electricity consumption is a vital foundation for smart energy management. Since the consumption of power varies with different appliances, better forecasting of power and peak demand is an essential accomplishment for the proper planning and development of the power generation and distribution system. This forecast analysis helps the service providers and the government to understand the lifestyle of the customers. The existing prediction and forecasting models are not meeting the standard requirements and moreover difficult to apply in practice. The forecast says that the boom of electric vehicles will increase the demand of electricity globally by 3% for the upcoming year. There exists a number of machine learning algorithms for classification and decision making. But the accuracy of the exiting methods have shown inferior performance in terms of prediction which leads to inefficient decision making in the quantity of electricity generation. This paper proposes the use of random forest supervised learning model to forecast the consumption of power and identify the level of peak demand. The large smart meter dataset collected at varying seasons of the year is fed to the random forest classifier technique for better analysis and forecasting. This approach outperforms in terms of accuracy, stability and generalization. In addition, this paper investigates the existing models and compares the performance with those models. The performance analysis shows that this model performs better than the other investigated models with performance accuracy of 95.67% and enhanced accuracy of precision and recall.