Power Consumption Forecast of Energy-intensive Enterprises Based on Power Marketing Business Data

Forecast of electricity is one of the most important topics in power system, it means a lot for power supply unit that forecast can be more accurate. Traditional methods always use single algorithm which has certain limitations. This article used two predict methods—back propagation (BP) neural network and combination of Auto-regressive Integrated Moving Average Model (ARIMA) and Support Vector Regression (SVR), considering macroeconomic indicators, yield of upstream and downstream products, weathers and other indicators to predict twenty energy-intensive enterprises’ electricity consumption. As a consequence, ARIMA+SVR performs better in predicting.