Power Flow Prediction for a Steel Plant with a Dynamic Regression Model

A power flow forecast it was shown for an industrial complex consisting of more than 20 different companies. The predominant consumer of electricity in the industrial complex is a steelworks company with an electric arc furnace. A steelworks with an electric arc furnace is a very specific example of an energy consumer. Other companies in the industrial complex are not connected to the steel plant technologically, but they are on the same energy connection. They have a weekly power flow profile significantly different from the steel plant. To calculate the forecast model and perform the forecast of power flows we need only two inputs of data: Historical measurements of power flows and the number of loads of the electric arc furnace in the following days. The first showed a prediction with linear regression. The next model to predict was the seasonal ARIMA model with a regressor, also called a dynamic regression model. The dynamic regression model improved the prediction by 15% compared to linear regression, according to the RMSE measure. This was followed by an improvement in the dynamic regression forecasting model by considering the seasonality 7/5 in the time series. We did this with a model with superimposed noise. With this model, we improved the forecasting by 30% to linear regression. Logically, the filter model of the prediction model also improved, gaining more Lag coefficients and losing a constant. Qualitatively, the result is a forecast of power flow for one month with prediction error MAPE 8% and measure R2 is 0.9.