Lastprognoser med Neurala Nätverk och Wavelets

Theodor Hugosson

Handledare: Jan Holst

Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology,
Lund University,

The aim of this thesis is to improve the prediction of the power consumption in Sweden 24 hours ahead. The technique used is to denoise the power load with wavelets and then feed the variables needed to describe the load to a neural network. The choice of variables to feed the neural network is determined with a test called deltatest. To evaluate the results, the power load is also predicted using a linear time-series approach.The result is that the use of wavelet based techniques does not seem to improve the quality of the prediction of the power consumption.
Keywords: Elförbrukningsprognos, neurala nätverk, wavelet, deltatest