Prediction of Heating Energy Consumption in Houses via Deep Learning Neural Network

Subject Areas : Analytical and Numerical Methods in Mechanical Design

Newsha Valadbeygi ¹ , Ali Shahrjerdi ²

1 - Faculty of Mechanical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
2 - Faculty of Mechanical Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

Received: 2022-10-12 Accepted : 2022-10-12 Published : 2022-12-01

Keywords: Neural network, heat transfer, energy consumption, deep learning,

Abstract :

This paper presents a novel model for prediction of energy consumption and heat transfer in houses on the basis of neural network by the use of experimental dataset of some cities of Iran for the learning process. To this end, a deep learning neural network (DNN) is designed by means of real set of data as input. In order to evaluate the proposed network, the predicted results are compared with the results obtained from the practical schemes. The comparison approved the effectiveness and feasibility of the suggested network in prediction of energy consumption and heat transfer with a low error for regression.

References:

1. Lee S, Cho S, Kim SH, Kim J, Chae S, Jeong H, Kim T. Deep neural network approach for prediction of heating energy consumption in old houses. Energies. 2020 Dec 28;14(1):122


2. . Duan J, Li F. Transient heat transfer analysis of phase change material melting in metal foam by experimental study and artificial neural network. Journal of Energy Storage. 2021 Jan 1;33:102160.


3.  Karakoyun Y, Acikgoz O, Çebi A, Koca A, Çetin G, Dalkilic AS, Wongwises S. A comprehensive approach to analyze the discrepancies in heat transfer characteristics pertaining to radiant ceiling heating system. Applied Thermal Engineering. 2021 Mar 25;187:116517.


4.  Cai S, Wang Z, Wang S, Perdikaris P, Karniadakis GE. Physics-informed neural networks for heat transfer problems. Journal of Heat Transfer. 2021 Jun 1;143(6).


5.  Zolghadri A, Maddah H, Ahmadi MH, Sharifpur M. Predicting parameters of heat transfer in a shell and tube heat exchanger using aluminum oxide nanofluid with artificial neural network (ANN) and self-organizing map (SOM). Sustainability. 2021 Aug 6;13(16):8824.


6.  Khan NA, Khalaf OI, Romero CA, Sulaiman M, Bakar MA. Application of Euler neural networks with soft computing paradigm to solve nonlinear problems arising in heat transfer. Entropy. 2021 Aug 16;23(8):1053.


7.  Khan MZ, Aziz M, Wijayanta AT. Prediction of heat transfer enhancement of delta-wing tape inserts using artificial neural network. Case Studies in Thermal Engineering. 2021 Oct 1;27:101322.


8. ] Bhattacharyya S, Sarkar D, Roy R, Chakraborty S, Goel V, Almatrafi E. Application of new artificial neural network to predict heat transfer and thermal performance of a solar air-heater tube. Sustainability. 2021 Jul 5;13(13):7477.


9.  Çolak AB, Karakoyun Y, Acikgoz O, Yumurtaci Z, Dalkilic AS. A numerical study aimed at finding optimal artificial neural network model covering experimentally obtained heat transfer characteristics of hydronic underfloor radiant heating systems running various nanofluids. Heat Transfer Research. 2022;53(5).


10.  Shoaib M, Raja MA, Farhat I, Shah Z, Kumam P, Islam S. Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer. Alexandria Engineering Journal. 2022 Feb 1;61(2):1607-23.


11.  Skrypnik AN, Shchelchkov AV, Gortyshov YF, Popov IA. Artificial neural networks application on friction factor and heat transfer coefficients prediction in tubes with inner helical-finning. Applied Thermal Engineering. 2022 Apr 1;206:118049.