Abstract:
In this paper, we applied the Wavelet Transform Coherence (WTC) and phase analysis to analyze the relationship between the
daily electricity demand (DED) and weather variables such as temperature, relative humidity, wind speed, and radiation. Te DED
data presents both seasonal fuctuations and increasing trend while the weather variables depict only seasonal variation. Te results
obtained from the WTC and phase analysis permit us to detect the period of time when the DED signifcantly correlates with the
weather variables. We found a strong seasonal interdependence between the air temperature and DED for a periodicity of 256-512
days and 128-256 days. Te relationship between the humidity and DED also shows a signifcant interdependence for a periodicity
of 256-512 days with average coherence equal to 0.8. Regarding the radiation and wind speed, the correlation is low with average
coherence less than 0.5.Tese results provide an insight into the properties of the impacts of weather variables on electricity demand
on the basis of which power planners can rely to improve their forecasting and planning of electricity demand.
