Model of capacity demand under uncertain weather
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.

Eelectrical distribution systems



Energy consumtion

Linear regression

Load forecasting

Risk management

Weather vulnerability


C.J. Wallnerström

Royal Institute of Technology (KTH)

J Setréus

Royal Institute of Technology (KTH)

P Hilber

Royal Institute of Technology (KTH)

F Tong

Northwest Electric Power Design Institute

Lina Bertling

Chalmers, Energy and Environment, Electric Power Engineering

2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010


Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering





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