Title :
Model of capacity demand under uncertain weather
Author :
Wallnerström, C.J. ; Setréus, J. ; Hilber, P. ; Tong, F. ; Bertling, L.
Author_Institution :
Sch. of Electr. Eng., KTH - R. Inst. of Technol., Stockholm, Sweden
Abstract :
Load forecasting is important in the operation of power systems. The characteristics of the electrical energy consumption are analyzed and its variation as an effect of several weather parameters is studied. Based on historical weather and consumption data received from a distribution system operator (DSO), numerical models of load forecasting are suggested according to electrical power consumption and on daily peak power respectively. Two linear regression models are presented: simple linear regression (SLR) with one input variable (temperature) and multiple linear regressions (MLR) with several input variables. The models are validated with historical data from other years. For daily peak power demand a MLR model has the lowest error, but for prediction of energy demand a SLR model is more accurate.
Keywords :
demand forecasting; load forecasting; power consumption; power distribution planning; regression analysis; capacity demand; distribution system operator; electrical power consumption; load forecasting; multiple linear regressions; simple linear regression; weather parameters; Energy consumption; Input variables; Linear regression; Load forecasting; Numerical models; Power system analysis computing; Power system modeling; Predictive models; Temperature; Weather forecasting; climate; component; electrical distribution systems; energy consumtion; linear regression; load Forecasting; risk management; weather vulnerabilit.y;
Conference_Titel :
Probabilistic Methods Applied to Power Systems (PMAPS), 2010 IEEE 11th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-5720-5
DOI :
10.1109/PMAPS.2010.5528841
