Energy Analysis of a Real Industrial Building: Model Development, Calibration via Genetic Algorithm and Monitored Data, Optimization of Photovoltaic Integration

Source:
By:Gerardo Maria Mauro

DOI: https://doi.org/10.30564/jcr.v1i1.830

Abstract:

This study performs the energy analysis of a real industrial building, located near Naples (South Italy). The used approach includes three phases: development of the energy model, model calibration based on monitored data and optimization of photovoltaic (PV) integration. Monitored data provide the monthly overall electricity demands of the facility for different years, while the load factors of industrial devices are not available. Thus, the assessment of hourly and daily trends of electricity demands and internal heat loads is not possible from monitored data. In order to solve such issue, the energy model of the building is developed under EnergyPlus environment, taking account of the existing PV system too. A genetic algorithm is run by coupling EnergyPlus and MATLAB® to properly calibrate the hourly load factors of the devices in order to achieve a good agreement between simulated and monitored values of monthly electricity demands. Finally, the installation of further PV panels is investigated to optimize the photovoltaic integration with a view to cost-effectiveness. The robustness of the optimization process is ensured using the calibrated energy model, which provides reliable hourly values of building electricity demand. Results show that the electricity produced by the additional PV panels is around 160 MWh per year, while the payback period is around 10 years demonstrating the financial viability of PV integration.

References:

[1] Attia S., Carlucci S., Hamdy M., O’Brien W. (2013). Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings, 60, 110-24. [2] Kheiri, F. (2018). A review on optimization methods applied in energy-efficient building geometry and envelope design. Renewable and Sustainable Energy Reviews, 92, 897-920. [3] Nguyen, A. T., Reiter, S., Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, 1043-58. [4] Wright, J., Alajmi, A. (2005, August). The robustness of genetic algorithms in solving unconstrained building optimization problems. In Proceedings of the 7th IBPSA Conference: Building Simulation, Montréal, Canada August (pp. 15-18). [5] Banos, R., Manzano-Agugliaro, F., Montoya, F. G., Gil, C., Alcayde, A., Gómez, J. (2011). Optimization methods applied to renewable and sustainable energy: A review. Renewable and sustainable energy reviews, 15(4), 1753-66. [6] Heo, Y., Choudhary, R., Augenbroe, G. A. (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47, 550-60. [7] Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., Vanoli, G. P. (2015). A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance. Energy and Buildings, 88, 78-90. [8] MathWorks, MATLAB – MATrixLABoratory. (2015). User's Guide (version 8.5.0). [9] EnergyPlus 8.6.0. Available online: https://github.com/NREL/EnergyPlus/releases/tag/v8.6.0 (accessed on 27 11 2018). [10] Royapoor, M., Roskilly, T. (2015). Building model calibration using energy and environmental data. Energy and Buildings, 94, 109-20. [11] ASHRAE Guideline 14-2014. (2014). Guideline 14-2014. Measurement of energy, demand, and water savings. American Society of Heating, Refrigerating, and Air Conditioning Engineers, Atlanta, GA. [12] Hong, T., Kim, J., Jeong, J., Lee, M., Ji, C. (2017). Automatic calibration model of a building energy simulation using optimization algorithm. Energy Procedia, 105, 3698-704. [13] Lara, R. A., Naboni, E., Pernigotto, G., Cappelletti, F., Zhang, Y., Barzon, F., ... Romagnoni, P. (2017). Optimization tools for building energy model calibration. Energy Procedia, 111, 1060-69. [14] Fan, Y., Xia, X. (2017). A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance. Applied Energy, 189, 327-35. [15] Cacabelos, A., Eguía, P., Febrero, L., Granada, E. (2017). Development of a new multi-stage building energy model calibration methodology and validation in a public library. Energy and Buildings, 146, 182-99. [16] Gao, H., Koch, C., Wu, Y. (2019). Building information modelling based building energy modelling: A review. Applied Energy, 238, 320-43. [17] Venkateswari, R., Sreejith, S. (2019). Factors influencing the efficiency of photovoltaic system. Renewable and Sustainable Energy Reviews, 101, 376-94. [18] Al-Addous, M., Dalala, Z., Class, C. B., Alawneh, F., Al-Taani, H. (2017). Performance analysis of off-grid PV systems in the Jordan Valley. Renewable Energy, 113, 930-41. [19] DesignBuilder Software – V. 5.0.3.7, DesignBuilder Software Ltd, Gloucestershire, UK, 2017. Available online: www.designbuilder.co.uk (accessed on 05 04 2018). [20] ASHRAE, 2002. American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE Handbook HVAC Applications, International weather for energy calculations (IWEC Weather Files) User’s Manual. U.S. Atlanta, GA 30329. [21] https://energyplus.net/weather (accessed on 27 11 2018). [22] Desideri, U., & Asdrubali, F. (Eds.). (2018). Handbook of Energy Efficiency in Buildings: A Life Cycle Approach. Butterworth-Heinemann. [23] Pan, Y., Huang, Z., Wu, G. (2007). Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai. Energy and Buildings, 39(6), 651-57. [24] Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., Vanoli, G. P. (2016). Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality. Applied energy, 174, 37-68. [25] Ascione, F., Bianco, N., De Masi, R. F., Mauro, G. M., Vanoli, G. P. (2017). Resilience of robust cost-optimal energy retrofit of buildings to global warming: A multi-stage, multi-objective approach. Energy and Buildings, 153, 150-167. [26] European Parliament and Council Directive 2010/31/UE on the energy performance of buildings. Available online: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A32010L0031 (accessed on 27 11 2018). [27] Commission Delegated Regulation No 244/2012. Available online: http://www.buildup.eu/sites/default/files/content/l_08120120321en00180036.pdf (accessed on 27 11 2018).