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. The DED data presents both seasonal fluctuations and increasing trend while the weather variables depict only seasonal variation. The results obtained from the WTC and phase analysis permit us to detect the period of time when the DED significantly 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. The relationship between the humidity and DED also shows a significant 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. These 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.

A multi-perspective analysis of microclimate dynamics for air-based solar hydrogen production

“…Several successful implementations of WTC exist within the framework of comparing several natural time series phenomena in both the time and frequency domain. These implementations range from coherence studies of electricity demand and weather data ( Bonkaney et al., 2019 ), arctic oscillation index and Baltic maximum sea ice score ( Grinsted et al., 2004 ), freshwater discharge and microclimate ( Labat, 2010 ), and computational expansions towards including machine learning and artificial intelligence techniques in mid-to long term electric load forecasting ( Zhang and Liu, 2010 ).…”

“…Assuming two time series, and , representing some fundamental elements of a microclimate, for example temperature and relative humidity, the WTC could be constructed using the following strategy ( Bonkaney et al., 2019 ): and with , , and representing the power of the wavelet analysis of two signals, the power of individual signals, and the phase difference between two signals. Eqs.…”

Anomaly Detection in Electricity Consumption Data using Deep Learning

“…Their study showed that climatic variables which are heating degree days, evaporation, humidity, and wind speed greatly affect the electricity demand. The authors in [14], concluded that daily electricity demand in Niamey varies both seasonally and from year to year, showing that temperature, humidity, and solar radiation have significant influence on electricity consumption. They also observed a very low coherence between wind speed and daily electricity consumption.…”