Análisis estadístico y modelo de pronóstico SARIMA aplicado al consumo de energía eléctrica en instalaciones universitarias
DOI:
https://doi.org/10.46842/ipn.cien.v26n2a03Palabras clave:
energy consumption, scholar buildings, time series forecasting, SARIMA modelsResumen
Analizar el comportamiento del consumo energético en edificios es fundamental para la implementación de medidas de ahorro y uso eficiente de la energía, sin perder atención al confort al interior de estos. En este estudio se realizó un análisis estadístico y de pronóstico con series de tiempo de la situación energética de un conjunto de edificios de una unidad académica universitaria de la Ciudad de México. Para el pronóstico se utilizaron modelos Estacionales Autorregresivos Integrados y de Medias Móviles (SARIMA) con datos del consumo de energía eléctrica de 55 meses y con estos se crearon particiones de entrenamiento y prueba que generaron dos modelos SARIMA. Los resultados mostraron una gran dependencia en el ciclo escolar del consumo de electricidad, además de un corrimiento en el ciclo en el primer año de estudio. El porcentaje de error absoluto medio (MAPE) para las particiones de entrenamiento creadas muestra que el mejor ajuste lo tiene el modelo SARIMA (3,1,1) (1,0,0)12 para la partición de 48 meses, mientras que el modelo SARIMA (2,1,2) (1,0,0)12 lo hace para la partición de prueba de 43 meses. Los intervalos de confianza para el pronóstico a 7 y 12 meses son menos amplios para el modelo SARIMA (3,1,1) (1,0,0)12 que para el modelo SARIMA (2,1,2) (1,0,0)12. Finalmente, el análisis estadístico y el modelado de series de tiempo permiten un mejor entendimiento del comportamiento energético del conjunto de edificios y fortalece la auditoría energética con miras a diseñar o aplicar medidas de ahorro o uso eficiente de la energía.
Referencias
EIA, "International Energy Outlook 2019", EIA, 2019, https://www.eia.gov/outlooks/ieo/tables_side.php
IEA, "World Energy Balances 2019", IEA, 2019, https://www.iea.org/data-and-statistics/data-product/world-energy-balances
EIA, "Annual Energy Outlook 2018", IEA,2018, https://www.eia.gov/outlooks/archive/aeo18/
CCEI, "Report on Energy Supply and Demand in Canada", CCEI, 2017, https://www150.statcan.gc.ca/n1/pub/57-003-x/57-003-x2020001-eng.htm
SENER, "Balance Nacional de Energía 2018", SENER, 2018, https://www.gob.mx/sener/documentos/balance-nacional-de-energia-2018
BP, "Statistical Review of the World Energy 2020", BP, 2020, https://www.bp.com/en/global/corporate/news-and-insights/press-releases/bp-statistical-review-of-world-energy-2020-published.html
IEA, "Energy and Climate Change 2015", IEA, 2015, https://iea.blob.core.windows.net/assets/8d783513-fd22-463a-b57d-a0d8d608d86f/WEO2015SpecialReportonEnergyandClimateChange.pdf
M. O. Fadeyi, K. Alkhaja, M. B. Sulayem, B. Abu-Hijleh, "Evaluation of indoor environmental quality conditions in elementary schools׳ classrooms in the United Arab Emirates", Frontiers of Architectural Research, vol. 3,no. 2, pp. 166-177, 2014, https://doi.org/10.1016/j.foar.2014.03.001
Z. Ma, P. Cooper, D. Daly, L. Ledo, "Existing building retrofits: Methodology and state-of-the-art", Energy and buildings, vol. 55, pp. 889-902, 2012, https://doi.org/10.1016/j.enbuild.2012.08.018
W. Chung, "Review of building energy-use performance benchmarking methodologies," Applied Energy, vol. 88, no. 5, pp. 1470-1479, 2011, https://doi.org/10.1016/j.apenergy.2010.11.022
T. Nikolaou, D. Kolokotsa, G. Stavrakakis, "Review on methodologies for energy benchmarking, rating and classification of buildings," Advances in Building Energy Research, vol. 5, no. 1, pp. 53-70, 2011, https://doi.org/10.1080/17512549.2011.582340
K. P. Amber, R. Ahmad, M. W. Aslam, A. Kousar, M. Usman, M. S. Khan, "Intelligent techniques for forecasting electricity consumption of buildings," Energy, vol. 157, pp. 886-893, 2018, https://doi.org/10.1016/j.energy.2018.05.155
J. Zhao, Y. Xin, D. Tong, "Energy consumption quota of public buildings based on statistical analysis," Energy Policy, vol. 43, pp. 362-370, 2012, https://doi.org/10.1016/j.enpol.2012.01.015
M. Raatikainen, J. P. Skön, K. Leiviskä, M. Kolehmainen, "Intelligent analysis of energy consumption in school buildings," Applied energy, vol. 165, pp. 416-429, 2016, https://doi.org/10.1016/j.apenergy.2015.12.072
H. Xiao, Q. Wei, Y. Jiang, "The reality and statistical distribution of energy consumption in office buildings in China," Energy and Buildings, vol 50, pp. 259-265, 2012, https://doi.org/10.1016/j.enbuild.2012.03.048
T. Sekki, M. Airaksinen, A. Saari, "Measured energy consumption of educational buildings in a Finnish city," Energy and Buildings, vol 87, pp. 105-115, 2015, https://doi.org/10.1016/j.enbuild.2014.11.032
A. Thewes, S. Maas, F. Scholzen, D. Waldmann, A. Zürbes, "Field study on the energy consumption of school buildings in Luxembourg," Energy and Buildings, vol. 68, pp. 460-470, 2014, https://doi.org/10.1016/j.enbuild.2013.10.002
B. Arregi, R. Garay, "Regression analysis of the energy consumption of tertiary buildings," Energy Procedia, vol. 122, pp. 9-14, 2017, https://doi.org/10.1016/j.egypro.2017.07.290
L. Brady, M. Abdellatif, "Assessment of energy consumption in existing buildings," Energy and Buildings, vol 149, pp. 142-150, 2017, https://doi.org/10.1016/j.enbuild.2017.05.051
H. Ma, N. Du, S. Yu, W. Lu, Z. Zhang, N. Deng, C. Li, "Analysis of typical public building energy consumption in northern China," Energy and Buildings, vol. 136, pp. 139-150, 2017, https://doi.org/10.1016/j.enbuild.2016.11.037
S. S. Amiri, M. Mottahedi, S. Asadi, "Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the US," Energy and Buildings, vol. 109, pp. 209-216, 2015, https://doi.org/10.1016/j.enbuild.2015.09.073
M. Mottahedi, A. Mohammadpour, S. S. Amiri, D. Riley, S. Asadi, "Multi-linear regression models to predict the annual energy consumption of an office building with different shapes," Procedia Engineering, vol. 118, pp. 622-629, 2015, https://doi.org/10.1016/j.proeng.2015.08.495
C. Deb, F. Zhang, J. Yang, S. E. Lee, K. W. Shah, "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, vol. 74, pp. 902-924, 2017, https://doi.org/10.1016/j.rser.2017.02.085
H. X. Zhao, F. Magoulès, "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 3586-3592, 2012, https://doi.org/10.1016/j.rser.2012.02.049
P. Chujai, N. Kerdprasop, K. Kerdprasop," Time series analysis of household electric consumption with ARIMA and ARMA models," In Proceedings of the International Multiconference of Engineers and Computer Scientists, vol. 1, pp. 295-300, 2013, http://www.iaeng.org/publication/IMECS2013/IMECS2013_pp295-300.pdf
M. Bourdeau, X. Qiang Zhai, E. Nefzaoui, X. Guo, P. Chatellier, "Modeling and forecasting building energy consumption: A review of data-driven techniques," Sustainable Cities and Society, vol. 48, pp. 101533, 2019, https://doi.org/10.1016/j.scs.2019.101533
X. Lü, T. Lu, C. J. Kibert, M. Viljanen," Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, vol. 144, pp. 261-275, 2015, https://doi.org/10.1016/j.apenergy.2014.12.019
A. S. Ahmad, M. Y. Hassan, M. P. Abdullah, H. A. Rahman, F. Hussin, H. Abdullah, R. Saidur, "A review on applications of ANN and SVM for building electrical energy consumption forecasting," Renewable and Sustainable Energy Reviews, vol. 33, pp. 102-109, 2014, https://doi.org/10.1016/j.rser.2014.01.069
D. Liu, Q. Chen, K. Mori, "Time series forecasting method of building energy consumption using support vector regression," In 2015 IEEE international conference on information and automation, pp. 1628-1632, Ago. 2015, https://doi.org/10.1109/ICInfA.2015.7279546
J. Hwang, D. Suh, M. O. Otto, "Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach," Energies, vol. 13, no. 22, pp. 5885, 2020, https://doi.org/10.3390/en13225885
Y. Chae, R. Horesh, Y. Hwang, Y. Lee, "Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings," Energy and Buildings, vol. 111, pp. 184-194, 2016, https://doi.org/10.1016/j.enbuild.2015.11.045
R. Mena, F. Rodríguez, M. Castilla, M. Arahal, "A prediction model based on neural networks for the energy consumption of a bioclimatic building," Energy and Buildings, vol. 82, pp. 142-155, 2014, https://doi.org/10.1016/j.enbuild.2014.06.052
C. Deb, L. Eang, J. Yang, M. Santamouris, "Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks," Energy and Buildings, vol. 121, pp. 284-297, 2016, https://doi.org/10.1016/j.enbuild.2015.12.050
Y. Cheng-wen, Y. Jian, "Application of ANN for the prediction of building energy consumption at different climate zones with HDD and CDD," In 2010 2nd International Conference on Future Computer and Communication, vol. 3, pp. V3-286-289, May. 2010, https://doi.org/10.1109/ICFCC.2010.5497626
R. Yokoyama, T. Wakui, R. Satake, "Prediction of energy demands using neural network with model identification by global optimization," Energy Conversion and Management, vol. 50, no. 2, pp. 319-327, 2009, https://doi.org/10.1016/j.enconman.2008.09.017
B. Dong, C. Cao, L. Lee, "Applying support vector machines to predict building energy consumption in tropical region," Energy and Buildings, vol. 37, no. 5, pp. 545-553, 2005, https://doi.org/10.1016/j.enbuild.2004.09.009
R. Jain, K. Smith, P. Culligan, J. Taylor, "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, vol. 123, pp. 168-178, 2014, https://doi.org/10.1016/j.apenergy.2014.02.057
F. Zhang, C. Deb, S. Lee, J. Yang, K. Shah, "Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique," Energy and Buildings, vol. 126, pp. 94-103, 2016, https://doi.org/10.1016/j.enbuild.2016.05.028
F. Wahid, D. Kim, "A prediction approach for demand analysis of energy consumption using k-nearest neighbor in residential buildings," International Journal of Smart Home, vol. 10, no. 2, pp. 97-108, 2016, https://doi.org/10.14257/ijsh.2016.10.2.10
L. Xuemei, D. Yuyan, D. Lixing, J. Liangzhong, "Building cooling load forecasting using fuzzy support vector machine and fuzzy C-mean clustering," In 2010 international conference on computer and communication technologies in agriculture engineering, vol. 1, pp. 438-441, Jun. 2010, https://doi.org/10.1109/CCTAE.2010.5543577
K. Yun, R. Luck, P. Mago, H., Cho, "Building hourly thermal load prediction using an indexed ARX model," Energy and Buildings, vol. 54, pp. 225-233, 2012, https://doi.org/10.1016/j.enbuild.2012.08.007
I. Korolija, Y. Zhang, L. Marjanovic-Halburd, V. Hanby, "Regression models for predicting UK office building energy consumption from heating and cooling demands," Energy and Buildings, vol. 59, pp. 214-227, 2013, https://doi.org/10.1016/j.enbuild.2012.12.005
Y. Zhang, Z. O'Neill, B. Dong, G. Augenbroe, "Comparisons of inverse modeling approaches for predicting building energy performance," Building and Environment, vol. 86, pp. 177-190, 2015, https://doi.org/10.1016/j.buildenv.2014.12.023
K. Jeong, C. Koo, T. Hong, "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," energy, vol. 71, pp. 71-79, 2015, https://doi.org/10.1016/j.energy.2014.04.027
H. Kaur, S. Ahuja, "Time Series Analysis and Prediction of Electricity Consumption of Health Care Institution Using ARIMA Model," Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol. 547, pp. 347–358, 2017, https://doi.org/10.1007/978-981-10-3325-4_35
K. Li, H. Su, J. Chu, "Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: A comparative study," Energy and Buildings, vol. 43, no. 10, pp. 2893-2899, 2011, https://doi.org/10.1016/j.enbuild.2011.07.010
M. Santamouris, G. Mihalakakou, P. Patargias, et al., "Using intelligent clustering techniques to classify the energy performance of school buildings," Energy and buildings, vol. 39, no. 1, pp. 45-51, 2007, https://doi.org/10.1016/j.enbuild.2006.04.018
W. Chung, "Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings," Applied energy, vol. 95, pp. 45-49, 2012, https://doi.org/10.1016/j.apenergy.2012.01.061
C. Fan, F. Xiao, S. Wang, "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, vol. 127, pp. 1–10, 2014, https://doi.org/10.1016/J.APENERGY.2014.04.016
O. Valgaev, F. Kupzog, "Building power demand forecasting using K-nearest neighbors' model – initial approach," 2016 IEEE PES Asia-Pacific power and energy engineering conference (APPEEC), pp. 1055–1060, 2016, https://doi.org/10.1109/APPEEC.2016
W. Ho, F. Yu, "Measurement and verification of energy performance for chiller system retrofit with k nearest neighbors regression," Journal of Building Engineering, vol. 46, pp. 103845, 2022, https://doi.org/10.1016/j.jobe.2021.103845
M. P. Deru, J. Kelsey, D. Pearson, Procedures for commercial building energy audits, 2nd ed. Atlanta, GA, E. U., ASHRAE, 2011, https://www.techstreet.com/ashrae/ashrae_books.html
T. Lawrence, A. K. Darwich, J. K. Means, D. Macauley, ASHRAE green guide: Design, construction, and operation of sustainable buildings, 5th ed. Atlanta, GA, E. U., ASHRAE, 2018, https://www.techstreet.com/ashrae/ashrae_books.html
R Foundation, R project, 2020,https://www.r-project.org/
Team RStudio, RStudio Desktop. Boston, MA, E. U., RStudio, 2022, https://www.rstudio.com/
R. H. Shumway, D. S. Stoffer, Time Series Analysis and Its Applications with R Examples, Fourth edition, New York, E. U., Springer Science+Business Media, 2017.
G. E. P. Box, G. M. Jenkins, G. C. Reinsel, G. M. Ljung, Time series analysis: forecasting and control, fifth edition, Hoboken, New Jersey, E. U., John Wiley & Sons, Inc., 2016.
C. Chatfield, H. Xing, The Analysis of Time Series: An Introduction with R, seventh edition, New York, E. U., Chapman & Hall, 2019.
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2022 Instituto Politécnico Nacional
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
