Understanding Consumers Heat Load Demand via Wavelet Transformation for Daily Production Planning Purposes

This research paper investigates the relationship between heat load consumption and meteorological factors such as outdoor temperature, humidity and windspeed since these factors are main drivers for heat production planning. To propose a good heat load forecasting model, it is crucial to understand the interaction between the heat consumption and the meteorological factors. This research paper presents alternative and more robust methods compared to the classical correlation analysis technique. We demonstrated sophisticated approach namely wavelet coherence to identify and understand the relationship between the heat consumption and meteorological factors during the analyzed time window. As a main benefit of a wavelet coherence we can mention that effect of outdoor temperature and humidity is noticeable in diurnal cycle. Also we found that wind speed as a predictor is useful mainly for long term prediction.