 Power plant condenser performance forecasting using a non-fully connected artificial neural networkM.m Prieto, E Montañés and O Menéndez Energy, 2001, vol. 26, issue 1, 65-79 Abstract: This paper presents a model that uses non-fully connected Feedforward Artificial Neural Networks (FANNs) for the forecasting of a seawater-refrigerated power plant condenser performance using the heat transfer rate (Q̇), the heat transfer coefficient (U) and the cleanliness factor (FC). The model developed includes FANNs that take into account the previous temporal values of the most important variables for obtaining the condenser performance, in order to forecast the next temporal value, as well as FANNs that relate the forecasted values with the corresponding condenser performance values Q̇, U and FC. In FANN architectures, the physical relationships between variables were taken into account. To analyze the model's performance, different ways of grouping data were used: high tide and low tide, right side water box and left side water box of the condenser and time step (daily and every three days). The errors in the test stage for Q̇, U and FC were acceptable, being less than 0.5% for Q̇, around 4% for U and around 2% for FC. The errors in the forecasting stage for U and FC increased with respect to the test stage. Date: 2001 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:26:y:2001:i:1:p:65-79 DOI: 10.1016/S0360-5442(00)00046-3 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.