A method for predicting the annual building heating demand based on limited performance data

Neural networks based predictive control for thermal comfort and energy savings in public buildings

“…Within this area, ANNs have been mainly applied in several aspects of HVAC control methodologies [5][6][7][8][9][10][11], and in forecasting energy consumption [12][13][14][15][16][17][18][19][20].…”

An intelligent approach to assessing the effect of building occupancy on building cooling load prediction

“…The traditional method used for simulating energy demand in buildings is based on the implementation of building characteristics in more or less ideal physical sub-processes [13]. Although this method provides for a good understanding of the physical environment, it requires a large number of parameters and different degrees of idealization and simplification.…”

Hybrid System for fouling control in biomass boilers

“…It is a simple and well-known type of NN but it was chosen because it fits perfectly with the problem to solve. Feedforward NN shows a great process capacity with a robust performance specially suitable to dealing with non-filtered and on-line real process data (Olofsson et al, 1998;Bechtler et al, 2001). Moreover, non-linear modelling of time series and forecasting utilization (Olofsson et al, 1998;Lu and Hogg, 2000;Mihalakakou et al, 2000) are another important NN characteristics that are appropriate for this problem.…”

“…Feedforward NN shows a great process capacity with a robust performance specially suitable to dealing with non-filtered and on-line real process data (Olofsson et al, 1998;Bechtler et al, 2001). Moreover, non-linear modelling of time series and forecasting utilization (Olofsson et al, 1998;Lu and Hogg, 2000;Mihalakakou et al, 2000) are another important NN characteristics that are appropriate for this problem. The general idea of the system is not to design a strong protocol to manage data for developing a ''white box'' (where the connection between inputs and outputs could be expressed using equations), but allow NN to deal with on-line real process data by means of a ''grey box'' model (Ikonen et al, 2000).…”

“…The introduction of the Neural Networks (NNs) to solve other problems concerned to power plants, power systems modelling, control and forecasting in energy systems has been progressively reported elsewhere (Olofsson et al, 1998;Lu and Hogg, 2000;Mihalakakou et al, 2000;Bechtler et al, 2001;Gareta et al, 2006). The application of the Artificial Intelligence (AI) techniques to renewable energies (Kalogirou, 2001(Kalogirou, , 2003 and to the evaluation of fouling deposits evolution inside the boilers (Afgan et al, 1996;Lalot and Lecoeuche, 2003) also have been reported.…”