Abstract
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%).
Highlights
The rise in the quantity and diversity of electronic devices makes linking active and reactive power demand harder for every customer [1]
In building 32, consumption was zero for a large part of the 3 years resulting in mostly zero baseline. We considered such a case as an exception and omitted the results of building 32 from Table 7
The multiplication adjustment with ‘X of Y high’ has the overall lowest average error over the considered years ranging between 9.95% and 12.97% annually
Summary
The rise in the quantity and diversity of electronic devices makes linking active and reactive power demand harder for every customer [1]. For such a reason, separate active and reactive power forecasting is a viable option. Consistency in calculation of the errors can be very important for comparing various algorithms [4] because it works like an indicator that is measured consistently. Coupling this with some sort of robust filtering like Hampel’s X84 rule [5,6]
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