Prediction of Electricity Load Demand in a Mediterranean Island Environment Based on the Physiologically Equivalent Temperature and Artificial Neural Networks Modeling

The goal of this work is to examine the potential for electricity load demand (ELD) hourly prediction with the use of artificial neural networks (ANNs), based on the Physiologically Equivalent Temperature (PET) index. The current research work investigates the relation between the PET index and electricity demand patterns in Mediterranean island regions and the Aegean Sea in specific. PET is based on the Munich Energy balance Model for Individuals, which describes the thermal conditions of the human body in a physiologically relevant way. Results obtained show that ANNs give an adequate prediction of hourly electricity load demand for Amorgos island (central Aegean Sea) at a statistical significant level of p<0.01.