Smart grid data analysis and prediction modeling

With the rapid development of power grid, prediction of electric quantity changes has become increasingly important. High-performance power grid systems can improve economic effectiveness and operational efficiency through accurate prediction. This paper proposes a prediction model based on temperature, humidity, time, and the number of people. On account of the standards of support vector machine (SVM) and the HBase platform, we have implemented a forecasting model and designed simulative experiments. The experimental results show that time and variation in the number of people has a remarkable influence on prediction, while temperature and humidity hardly have any effects.