Model of capacity demand under uncertain weather
Other conference contribution, 2010
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. © 2010 IEEE.
Linear regression
Component
Risk management
Energy consumtion
Climate
Weather vulnerability
Eelectrical distribution systems
Load forecasting
Author
C.J. Wallnerström
The Royal Institute of Technology (KTH)
J Setréus
The Royal Institute of Technology (KTH)
P Hilber
The Royal Institute of Technology (KTH)
F Tong
Northwest Electric Power Design Institute
Lina Bertling Tjernberg
Chalmers, Energy and Environment, Electric Power Engineering
2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010
314-318
978-142445723-6 (ISBN)
Subject Categories (SSIF 2011)
Electrical Engineering, Electronic Engineering, Information Engineering
DOI
10.1109/PMAPS.2010.5528841
ISBN
978-142445723-6