neural networks

This paper presents two approaches used to develop a model of Room Storage Heater. The first one consists of a dynamic model of the RSH developed by the authors using the results obtained from tests performed in a calorimetric chamber. The model was verified against the results obtained during five different charge-discharge test periods. The second approach is a new concept based on Neural Networks applications. In this approach, we suppose that we do not have a description of the RSH itself.

The authors have created a Neural-Fuzzy Assistant which acts as a Decision Support System and helps to perform quickly and easily the estimations of office building energy consumption. The Neural-Fuzzy Assistant presented in this paper allows the user to determine the impact of eleven building parameters on the electrical annual and monthly energy consumption, annual and monthly maximum electrical demand and cooling and heating annual consumption and demand.

The effect of the synoptic scale atmospheric circulation on the urban heat island phenomenon over Athens, Greece, was investigated and quantified for a period of two years, using a neural network approach. A neural network model was appropriately designed and tested for the estimation of the heat island intensity at twenty-three stations during the examined period. The day-by-day synoptic scale atmospheric circulation in the lower atmosphere for the same period was classified into eight statistically distinct categories.