Abstract
The majority of opportunities to energy savings today and in the next decades are in the existing building stock, including commercial and retail ones. These are constrained by old equipment, aging infrastructure, and inadequate operations resources. In particular, we focus on a retail store located in Lisbon, El Corte Inglés, to reduce its electricity consumption while maintaining the same level of service and continuing to be a profitable business. Improving the operation of the air-handling unit (AHU) and the operation schedule of floors illumination is our focus. Data provided had long-term records of variables as the building power consumption, exterior temperature, solar irradiation, elevators, and HVAC system. Two artificial neural networks (ANN) models were developed to predict the building power consumption and indoor average temperature, which allowed verifying the impact of small variations in the AHU electricity consumption. We deal both with the indoor average temperature (comfort) and power consumption of AHU to reduce the electric energy cost. The study leads to a decrease of 9.6% of the total electricity consumption, with an average indoor temperature increasing by only 0.15 °C. The total potential savings considering all the building 48 AHUs resulted in very good savings of 34,000 euros per year.
Similar content being viewed by others
References
Chua, K.J., Chou, S.K.: A performance-based method for energy efficiency improvement of buildings. Energy Convers Manag 52(4), 1829–1839 (2011)
Shirazi, A., Taylor, R., Morrison, G., White, S.: A comprehensive, multi-objective optimization of solar-powered absorption chiller systems for air-conditioning applications. Energy Convers Manag 132(15), 281–306 (2017)
Pisello, A.L., Bobker, M., Cotana, F.: A building energy efficiency optimization method by evaluating the effective thermal zones occupancy. Energies 5(12), 5257–5278 (2012)
Ryzhov, A., Ouerdane, H., Gryazina, E., Bischi, A., Turitsyn, K.: Model predictive control of indoor microclimate: Existing building stock comfort improvement. Energy Convers Manag 179, 219–228 (2019)
Erickson, V.L. et al.: Energy efficient building environment control strategies using real-time occupancy measurements. In: Proceedings of the First ACM workshop on embedded sensing systems for energy-efficiency in buildings. ACM, (2009)
Anand, P., Cheong, D., Sekhar, C., Santamouris, M., Kondepudi, S.: Energy saving estimation for plug and lighting load using occupancy analysis. Renew Energy 143, 1143–1161 (2019)
Persson, J.: Low-energy buildings: energy use, indoor climate and market diffusion. Ph.D. thesis, KTH (2014)
Le, C.V., et al.: Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems. Trans Inst Measure Control 35(5), 583–592 (2013)
Runge, J., Zmeureanu, R.: Forecasting energy use in buildings using artificial neural networks: a review. Energies 12(17), 3254 (2019)
Mohandes, S.R., Zhang, A., Mahdiyar, A.: A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing 340, 55–75 (2019)
Deb, C., Siew-Eang, L., Santamouris, M.: Using artificial neural networks to assess HVAC related energy saving in retrofitted office buildings. Sol Energy 163, 32–44 (2018)
Deb, C., Frei, M., & Schlueter, A. (2020). Identifying temporal properties of building components and indoor environment for building performance assessment. Build Environ 168:106506.
Kaklauskas, A., Dzemyda, G., Tupenaite, L., Voitau, I., Kurasova, O., Naimaviciene, J., Kanapeckiene, L.: Artificial neural network-based decision support system for development of an energy-efficient built environment. Energies 11(8), 1994 (2018)
Heidarinejad, M., Cedeño-Laurent, J.G., Wentz, J.R., Rekstad, N.M., Spengler, J.D., Srebric, J.: Actual building energy use patterns and their implications for predictive modeling. Energy Convers Manag 144, 164–180 (2017)
Terés-Zubiaga, J., Pérez-Iribarren, E., González-Pino, I., Sala, J.M.: Effects of individual metering and charging of heating and domestic hot water on energy consumption of buildings in temperate climates. Energy Convers Manag 171(2018), 491–506 (2018)
Appraisal and evaluation of energy utilization and efficiency in the kingdom of Saudi Arabia, volume 2: Energy efficiency audit: Case studies. King Abdullah University of Science and Technology (KAUST)
A bright idea to reduce energy use. http://www.sonaesierra.com/media/144883/a_bright_idea_to_reduce_energy_use.pdf. Accessed 24 Aug 2019
Turning on the lights for a win-win energy efficiency project. https://www.sonaesierra.com/media/144913/turningonthelightsforawin-winenergyefficiencyproject.pdf. Accessed 24 Aug 2019
Reducing operating costs through environmental improvements at loop5. https://www.sonaesierra.com/publicdocs/casestudies13/Reducing-operating-costs-through-environmental-improvements-at-Loop5.pdf. Accessed 24 Aug 2019
Sello shopping mall—putting energy savings back into your facility. https://www.downloads.siemens.com/download-center/Download.aspx?pos=download&fct=getasset&id1=A6V10356050. Accessed 24 Aug 2019
Salem, R., Bahadori-Jahromi, A., Mylona, A., Godfrey, P., Cook, D.: Investigating the potential impact of energy-efficient measures for retrofitting existing UK hotels to reach the nearly zero energy building (nZEB) standard. Energy Effic 12, 1–18 (2019)
Casteleiro-Roca, J.-L., Gómez-González, J.F., Calvo-Rolle, J.L., Jove, E., Quintián, H., Gonzalez-Diaz, B., Mendez Perez, J.A.: Short-term energy demand forecast in hotels using hybrid intelligent modeling. Sensors 19, 2485 (2019)
Gaspari, J., Fabbri, K., Gabrielli, L.: Retrofitting Hospitals: a parametric design approach to optimize energy efficiency. In: Earth and environmental science, IOP Conference Series, Vol. 290. IOP Publishing, p. 012130 (2019)
Silenzi, F., Priarone, A., Fossa, M.: Hourly simulations of a hospital building for assessing the thermal demand and the best retrofit strategies for consumption reduction. Therm Sci Eng Prog 6, 388–397 (2018)
Lee, J., Shepley, M.M., Choi, J.: Exploring the effects of a building retrofit to improve energy performance and sustainability: a case study of Korean public buildings. J Build Eng 25, 100822 (2019)
Zachariadis, T., Michopoulos, A., Vougiouklakis, Y., Piripitsi, K., Ellinopoulos, C., Struss, B.: Determination of cost-effective energy efficiency measures in buildings with the aid of multiple indices. Energies 11(1), 191 (2018)
Ganguly, S., Ahmed, A., Wang, F.: Optimised building energy and indoor microclimatic predictions using knowledge-based system identification in a historical art gallery. Neural Comput Appl (2019). https://doi.org/10.1007/s00521-019-04224-7
Tsai, P.H., Lin, C.T.: How should national museums create competitive advantage following changes in the global economic environment? Sustainability 10(10), 3749 (2018)
Ferrarese, S., Bertoni, D., Dentis, V., Gena, L., Leone, M., Rinaudo, M.: Microclimatic analysis in the Museum of Physics, University of Turin, Italy: a case-study. Eur Phys J Plus 133(12), 538 (2018)
Ferdyn-Grygierek, J., Grygierek, K.: Proposed strategies for improving poor hygrothermal conditions in museum exhibition rooms and their impact on energy demand. Energies 12(4), 620 (2019)
Kim, D.B., Kim, D.D., Kim, T.: Energy performance assessment of HVAC commissioning using long-term monitoring data: a case study of the newly built office building in South Korea. Energy Build 204, 109465 (2019)
Verhelst, J., et al.: Model selection for continuous commissioning of HVAC-systems in office buildings: a review. Renew Sustain Energy Rev 76, 673–686 (2017)
Yang, C., et al.: A practical solution for HVAC prognostics: failure mode and effects analysis in building maintenance. J Build Eng 15, 26–32 (2018)
Zhao, J., Duan, Y., Liu, X.: Uncertainty analysis of weather forecast data for cooling load forecasting based on the Monte Carlo method. Energies 11(7), 1900 (2018)
Sihvonen, S.: AHU fault simulations. Granlund Consulting, Helsinki (2018)
Lee, J.M., et al.: Application of artificial neural networks for optimized AHU discharge air temperature set-point and minimized cooling energy in HVAC system. Appl Therm Eng 153, 726–738 (2019)
Funding
This work was supported by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2019.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Poço, E.R.G., Sousa, J.M.C. & Branco, P.J.C. Improving the energy efficiency of aging retail buildings: a large department store in Lisbon as case study. Energy Syst 12, 1081–1111 (2021). https://doi.org/10.1007/s12667-020-00377-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12667-020-00377-w