The Analysis and Prediction of Power Consumption in Industry Based on Machine Learning

Abstract

Electricity consumption analysis and prediction are of great significance for power grid planning and the management of enterprise resources. Based on the historical power consumption data collected by the cloud platform of Fujian Huatuo energy management system, this paper analyzes the power consumption of the three floors of Fujian Huatuo Automation Technology. The results show that the biggest influencing factors are weather conditions and working days. Observing the fact that the pattern of power consumption has a great difference when the temperature grows larger than a threshold, this paper proposes a clustering-based prediction mechanism to predict the power consumption with different models. The experimental results show that the proposed mechanism can effectively improve the prediction accuracy of power consumption.