Wavelet Analysis of Daily Energy Demand and Weather Variables

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.