Forecasting Operational Parameters of a Solar Space Heating System using a Novel Multistage Artificial Neural Network

ADMT Journal

مقاله 8، دوره 11، شماره 2، شهریور 2018، صفحه 75-84   

نوع مقاله: Original Article

نویسندگان

Farnaz Jamadi^{* 1}؛ Behnam Jamali²

¹Department of physics, Sirjan university of technology

²Mechanical engineering, Sirjan University of Technology

چکیده

In this study, several operational parameters of a solar energy system are predicted through using a multistage ANN model. To achieve the best design of this model, three different back-propagation learning algorithms, i.e. Levenberg-Marquardt (LM), Pola-Riber Conjugate Gradient (CGP) and the Scaled Conjugate Gradient (SCG) are utilized. Further, to validate the ANN results, some experimental tests have been done in winter 2016 on a solar space heating system (SSHS) equipped with a parabolic trough collector (PTC). In the proposed model, ANN comprises three consecutive stages, while the outputs of each one are considered to be the inputs of the next. Results show that the maximum error rate in Stages 1, 2, and 3 has occurred in the LM algorithm with respectively 10, 6, and 10 neurons. Moreover, the best obtained determination coefficient of all stages belongs to the total system efficiency and has the value 0.999934 for LM-10. As a result, the multistage ANN model can simply forecast operational parameters of the solar energy systems with high accuracy.

کلیدواژه‌ها

Multistage neural network؛ Operational parameters؛ Solar space heating system؛ Total system efficiency

مراجع

[1]     Alamdari, P., Nematollahi, O., and Alemrajabi, A., Solar Energy Potentials in Iran: A Review, Renewable and Sustainable Energy Reviews, Vol. 21, No. 5, 2013, pp. 778-788, http://dx.doi.org/10.1016/j.rser.2012.12.052.

[2]     Dehghani sanij, R., Soltani, M. and Raahemifar, K., A New Design of Wind Tower for Passive Ventilation in Buildings to Reduce Energy Consumption in Windy Regions, Renewable and Sustainable Energy Reviews, Vol. 42, Feb., 2015, pp 182–195, http://dx.doi.org/10.1016/j.rser.2014.10.018.

[3]     Jovanovic, R. Z., Sretenovic, A. A. and Zivkovic, B. D., Ensemble of Various Neural Networks for Prediction of Heating Energy Consumption, Energy Buildings, Vol. 94, No. 1, 2015, pp. 189-199, https://doi.org/10.1016/j.enbuild.2015.02.052.

[4]     Ascione, F., Bianco, N., Stasio, C. D., Mauro, G. M. and Vanoli, G. P., Artificial Neural Networks to Predict Energy Performance and Retrofit Scenarios for Any Member of a Building Category: A Novel Approach, Energy, Vol. 118, No. 1, 2017, pp. 999-1017, http://dx.doi.org/10.1016/j.energy.2016.10.126.

[5]     Sholahudin S. and Han, H., Simplified Dynamic Neural Network Model to Predict Heating Load of a Building Using Taguchi Method, Energy, Vol. 115, No. 3, 2016, pp. 1672-1678,  https://doi.org/10.1016/j.energy.2016.03.057.

[6]     Deb, C., Eang, L. S., Yang, J. and Santamouris, M., Forecasting Diurnal Cooling Energy Load for Institutional Buildings Using Artificial Neural Networks, Energy and Buildings, Vol. 121, No. 1, 2016, pp. 284-297, https://doi.org/10.1016/j.enbuild.2015.12.050.

[7]     Argirioua, A. A., Bellas Velidisb, I., Kummert, M. and Andre´c, P., A Neural Network Controller for Hydronic Heating Systems, Neural Networks, Vol. 17, No. 3, 2004, pp. 427-440, doi:10.1016/j.neunet.2003.07.001.

[8]     Kalogirou, A. S., Assessment and Simulation Tools for Sustainable Energy Systems, Springer, London, 2013.

[9]     Boukelia, T. E., Arsalan, O. and Mecibah, M. S., ANN-Based Optimization of a Parabolic Trough Solar Thermal Power Plant, Applied Thermal Engineering, Vol. 107, No. 1, 2016, pp. 1210-1218, http://dx.doi.org/10.1016/j.applthermaleng.2016.07.084 .

[10]  Hirvonen, J., Rehman, H., Deb, K. and Sirén, K., Neural Network Metamodelling in Multi-Objective Optimization of a High Latitude Solar Community, Solar Energy, Vol. 155, No. 1, 2017, pp. 323–335, http://dx.doi.org/10.1016/j.solener.2017.06.040.

[11]  Yaïci, W., Evgueniy, E., Longo, M., Brenna, M. and Federica, F., Artificial Neural Network Modelling for Performance Prediction of Solar Energy System, Proceeding of 4th International Conference on Renewable Energy, Research and Applications, Palermo, Italy, 2015, pp. 22-25.

[12]  Liu, Z., Li, H., Zhang, X., Jin, G. and Cheng, K., Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine, Energies, Vol. 8, No. 8, 2015, pp. 8814-8834, https://doi.org/10.3390/en8088814.

[13]  Elminir, H. K., Areed, F. F. and Elsayed, T. S., Estimation of Solar Radiation Components Incident on Helwan Site using Neural Networks, Solar Energy, Vol. 79, No. 3, 2005, pp. 270–279, doi:10.1016/j.solener.2004.11.006

[14]  Mellit, A., Pavan, A. M., A 24-h Forecast of Solar Irradiance using Artificial Neural Network: Application for Performance Prediction of a Grid-Connected PV Plant at Trieste, Italy, Solar Energy, Vol. 84, No. 5, 2010, pp. 807–821, doi:10.1016/j.solener.2010.02.006.

[15]  Arat, H., Arslan, O., Optimization of District Heating System Aided by Geothermal Heat Pump: A Novel Multistage with Multilevel ANN Modelling, Applied Thermal Engineering, Vol. 111, No. 1, 2017, pp. 608-623, http://dx.doi.org/10.1016/j.applthermaleng.2016.09.150.

[16]  American Society of Heating, R. a. C., International Standard-Solar Energy-Solar Thermal Collector-Test Methods, Geneva, ASHRAE, Iso9806, 2014.

[17]  Duffi, J. A., Backman, W. A., Solar Engineering of Thermal Processes, 3nd ed, Wiley, Wisconsin, Madison, 2006, Chaps. 3, Vol. 6.