In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.
Revised: February 26, 2020 |
Published: July 17, 2016
Citation
Sun Y., Z. Hou, D. Meng, N.A. Samaan, Y.V. Makarov, and Z. Huang. 2016.Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting. In IEEE Power and Energy Society General Meeting (PESGM 2016), July 17-21, 2016, Boston, MA. Piscataway, New Jersey:IEEE.PNNL-SA-114275.doi:10.1109/PESGM.2016.7741272