Prediction and Optimization of Intelligent Energy Consumption Patterns in the Power Internet of Things Based on Big Data Analysis

With the rapid growth of global energy consumption and the promotion of environmental protection, energy conservation and emission reduction has become one of the main challenges. Power Internet of Things (IoT) is an important part of smart grid, which is of great significance to the efficient management and optimization of energy. The purpose of this study is to predict energy consumption patterns and optimize smart energy consumption through big data analysis. Through in-depth analysis and discussion on the key technologies and functional system of the prediction and optimization method of big data platform, this study has the following innovations and contributions. On the basis of forecasting and optimizing the system framework, a large amount of electricity consumption information was collected by using advanced data acquisition technology. Through data preprocessing, data noise and data missing were removed or filled. Feature information was extracted, and then machine learning algorithm based on big data was used to predict energy consumption patterns. The random forest (RF) algorithm was used to optimize energy consumption, and the internal mechanism of the random forest algorithm was analyzed. Finally, the key factors affecting energy consumption were discussed in detail. The results showed that in the first part of intelligent energy consumption pattern prediction, RF algorithm was compared with three evaluation indexes of support vector machine (SVM) and gradient boosting machine (GBM), that is, peak electricity consumption, renewable energy penetration rate and virtual dispatching capacity. The result is that the three evaluation indexes give excellent reports. The RF algorithm of intelligent energy consumption pattern, prediction and optimization can predict the energy consumption pattern more accurately and optimize the energy distribution of the power grid, with the highest prediction accuracy of 98.8%. Practice has proved that the intelligent energy consumption, pattern prediction and optimization method based on big data analysis can effectively promote the sustainable utilization of energy and provide a new way for intelligent management of power system.