Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: 'X of Y', weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. 'Addition X of Y middle' and 'Addition Weighted average' better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while 'Multiplication X of Y high' and 'Multiplication X of Y middle' better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).

Automated summary

This paper analyzes the most common forecasting algorithms used in electrical grids and demonstrates clear differences in accuracy between forecasting active and reactive power demand.