The objective of the present work is the medium, short and very short-term prognosis of load demand (LD) for the small-scale island of Tilos in Greece. For this purpose, Artificial Neural Network (ANNs) models were developed to forecast the LD of Tilos for different prediction horizons and time intervals, these covering the cases of 24 h ahead in hourly intervals (medium term prognosis), 2 h ahead in 10-min intervals (short term prognosis) and 10-min ahead in 1-min intervals (very short term prognosis). At the same time, stochastic/persistence autoregressive (AR) models were also developed and compared with the respective ANN models with regards to the LD prediction results obtained. For the training of the developed ANNs, meteorological data covering the period 2015–2017 were used, which had been recorded in 1-min intervals by two meteorological masts installed on the island Tilos. Furthermore, the biometeorological human thermal comfort-discomfort index, known as the cooling power index (CP), was also estimated and introduced in the training procedure of the forecasting models, while, for the evaluation of both AR and ANN forecasting models, well established statistical evaluation indices were applied. To this end, results show that in all cases covered, i.e. for both medium and short-term prognoses, the developed ANN forecasting models present a remarkable ability to predict the local LD of the island with high accuracy, enabling in this way the development of advanced energy management tools for both end-users and the system operators.