Optimizing Heat Pump Operation of Residential Buildings Using Calibrated R-C and Deep Learning Models and Electricity Costs Forecasts

Authors

DOI:

https://doi.org/10.52825/isec.v1i.1142

Abstract

The aim of this research is to improve the efficiency of energy systems using the mass of the building as thermal storage. We present a case study of a residential building, in which a detailed monitoring system was installed to measure, among other parameters, the electricity consumption, the indoor air quality, and the operation of the heating system, consisting on a Heat Pump (HP) and a radiant floor. Based on the data collected, both a lumped parameter model (R-C Model) and a Deep Learning (DL) Model have been calibrated to simulate the apartment analyzed. Both models provide a significantly accurate simulation of the apartment under real operating conditions. Then, using the simulation models, different operation scenarios have been analyzed. One of the scenarios considers the thermal inertia of the apartment and the electricity costs forecast to optimize the operation of the HP. Within this scenario, energy savings up to a 35.1%, and electricity costs savings up to a 47.3%, may be achieved during a winter season, when compared to the standardized operation of the HP.

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References

Intergovernmental Panel on Climate Change, “Synthesis Report of the IPCC Sixth Assessment Report (Ar6),” 2023.

European Commission, “Renovation Wave - The European Green Deal,” 2020. doi: 10.2833/535670.

The European Parliament and the Council of the EU, Directive (EU) 2018/844 of the European Parliament and of the Council of 30 May 2018 amending Directive 2010/31/EU on the energy performance of buildings and Directive 2012/27/EU on energy efficiency. 2018. [Online]. Available: https://eur-lex.europa.eu/eli/dir/2018/844/oj

European Commission, The European Green Deal, COM (2019) 640 Final. 2019. [Online]. Available: https://eur-lex.europa.eu/legal-content/ES/TXT/?uri=COM%3A2019%3A640%3AFIN

European Commission, A Renovation Wave for Europe - greening our buildings, creating jobs, improving lives, COM(2020) 662 Final. 2020. [Online]. Available: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020DC0662

International Energy Agency, “IEA ES Task 43: Storage for renewables and flexibility through standardized use of building mass.” https://nachhaltigwirtschaften.at/en/iea/technologyprogrammes/es/iea-es-task-43.php (accessed Oct. 23, 2023).

Z. Afroz, G. M. Shafiullah, T. Urmee, and G. Higgins, “Modeling techniques used in building HVAC control systems: A review,” 2017, doi: 10.1016/j.rser.2017.10.044.

R. Z. Homod, “Review on the HVAC System Modeling Types and the Shortcomings of Their Application,” J. Energy, vol. 2013, 2013, doi: 10.1155/2013/768632.

J. Terés-Zubiaga, C. Escudero, C. García-Gafaro, and J. Sala, “Methodology for evaluating the energy renovation effects on the thermal performance of social housing buildings: Monitoring study and grey box model development,” Energy Build., vol. 102, pp. 390–405, 2015, doi: 10.1016/j.enbuild.2015.06.010.

V. Dimitriou, S. K. Firth, T. M. Hassan, and T. Kane, “The applicability of Lumped Parameter modelling in houses using in-situ measurements,” Energy Build., vol. 223, p. 110068, 2020, doi: 10.1016/j.enbuild.2020.110068.

J. M. Zepter, A. Lüth, P. Crespo Del Granado, and R. Egging, “Prosumer integration in wholesale electricity markets: Synergies of peer-to-peer trade and residential storage,” Energy Build., vol. 184, pp. 163–176, 2019, doi: 10.1016/j.enbuild.2018.12.003.

I. Rabiu, N. Salim, A. Da’u A, and M. Nasser, “Modeling sentimental bias and temporal dynamics for adaptive deep recommendation system,” Expert Syst. Appl., vol. 191, p. 116262, 2022, doi: 10.1016/j.eswa.2021.116262.

H. Yu, F. Zhong, Y. Du, Y. Wang, X. Zhang, and S. Huang, “Short-term cooling and heating loads forecasting of building district energy system based on data-driven models,” Energy Build., vol. 298, p. 113513, 2023, doi: 10.1016/j.enbuild.2023.113513.

S. Yang, M. P. Wan, W. Chen, B. Feng Ng, and S. Dubey, “Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization,” 2020, doi: 10.1016/j.apenergy.2020.115147.

Universidad del País Vasco/Euskal Herriko Unibertsitatea and U. of Vigo, “DeepSmart Project.” https://deepsmart.webs.uvigo.es/ (accessed Oct. 23, 2023).

M. Cordeiro-Costas, D. Villanueva, P. Eguía-Oller, M. Martínez-Comesaña, and S. Ramos, “Load Forecasting with Machine Learning and Deep Learning Methods,” Appl. Sci., vol. 13, no. 13, 2023, doi: 10.3390/app13137933.

Ministerio de Fomento, “CTE-HE. Código Técnico de la Edificación. Documento Basico HE Ahorro de energia,” June, p. 68, 2017.

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Published

2024-04-09

How to Cite

Hernandez-Cruz, P., Escudero-Revilla, C., Cordeiro-Costas, M., Erkoreka-Gonzalez, A., Giraldo-Soto, C., Pérez-Orozco, R., & Eguía-Oller, P. (2024). Optimizing Heat Pump Operation of Residential Buildings Using Calibrated R-C and Deep Learning Models and Electricity Costs Forecasts. International Sustainable Energy Conference - Proceedings, 1. https://doi.org/10.52825/isec.v1i.1142

Conference Proceedings Volume

Section

Heat Pumping Technologies and System Integration