Calibrating numerical model by neural networks: A case study for the simulation of the indoor temperature of a building
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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2015
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en
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Energy Procedia, Volume 75, issue August, pp. 1366-1372
Abstract
This paper proposes a method using neural networks to calibrate numerical models. The approach passes the output of numerical model to a neural network for calibration. An experimental study was conducted using a simulation of unheated and uncooled indoor temperature of a sports hall. The proposed neural network-based model improves the results and produces more accurate calibrated indoor temperature. Furthermore, the developed calibration method requires only measurements of indoor temperatures as the necessary inputs, thus significantly simplifying the calibration procedure needed to model the building performances.Description
Keywords
Numerical model, Neural networks, Model calibration, Generalization, Unheated and uncooled indoor temperature simulation
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Citation
Lü, X, Lu, T & Viljanen, M 2015, ' Calibrating numerical model by neural networks: A case study for the simulation of the indoor temperature of a building ', Energy Procedia, vol. 75, no. August, pp. 1366-1372 . https://doi.org/10.1016/j.egypro.2015.07.215