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Machine Learning Algorithms Based for Demand-Side Energy Forecasting of an Office Building Loads

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Smart Applications and Data Analysis (SADASC 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2168))

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Abstract

Nowadays, buildings are increasingly turning to photovoltaic energy to improve their use of natural resources. This creates remarkable energy consumption, which can manifest itself in many forms: electrical loads, lighting loads, air-conditioning loads, and many others. In our article, we have studied a database containing the energy consumption of an office built in 2015 in Berkeley, California. As this database has already been prepared for prediction purposes, we have worked on predicting load types as a function of the time parameter. In this paper we studied the performance of some selected artificial intelligence models to predict the different types of loads in the last quarter-hour of the day as a function of other times periods recorded. Several models were tested, and the aim was to distinguish the best model in terms of performance and accuracy. We treated each type of load separately in the prediction for both sides of the building, namely: various electrical loads north and south sides, lighting in the south wing of the building and the Heating Ventilation and Air Conditioning loads for both South and North Wings of the building. Taking results in account, the Random Forest (RF) could be considered as a relevant model for the presented dataset for all load types, with an R2_score of around 0.9859 and no less than 0.89. Thus, due to its notable ability to track intensive fluctuations.

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References

  1. Schlindwein, L.F., Montalvo, C.: Energy citizenship: accounting for the heterogeneity of human behaviours within energy transition. Energy Policy 180, 113662 (2023). https://doi.org/10.1016/J.ENPOL.2023.113662

    Article  Google Scholar 

  2. Milin, I.A., Claudia, M., Pupazan, M., Rehman, A., Chirtoc, I.E., Ecobici, N.: Examining the Relationship between Rural and Urban Populations’ Access to Electricity and Economic Growth: A New Evidence‏. mdpi.com‏ (2022). https://doi.org/10.3390/su14138125

  3. Mrabet, Z., Alsamara, M., Saleh, A.S., Anwar, S.: Urbanization and non-renewable energy demand: a comparison of developed and emerging countries. Energy 170, 832–839 (2019). https://doi.org/10.1016/j.energy.2018.12.198

    Article  Google Scholar 

  4. Madlener, R., Sunak, Y.: Impacts of urbanization on urban structures and energy demand: what can we learn for urban energy planning and urbanization management? Sustain. Cities Soc. (2011). https://doi.org/10.1016/j.scs.2010.08.006

    Article  Google Scholar 

  5. Ohara, K.: Our Globally Changing Climate. Climate Change in the Anthropocene, pp. 3–18 (2022). https://doi.org/10.1016/B978-0-12-820308-8.00005-2

  6. Abbass, R.A., Kumar, P., El-Gendy, A.: An overview of monitoring and reduction strategies for health and climate change related emissions in the middle east and North Africa region. Atmos. Environ. 175, 33–43 (2018)

    Google Scholar 

  7. Ghezloun, A., Saidane, A., Oucher, N., Merabet, H.: Contribution of the Conferences of the Parties and the Renewable Energy Role for the Fight against Climate Change. AIP Conf. Proc., 1758 (2016). https://doi.org/10.1063/1.4959426/690420

  8. Żywiołek, J., Rosak-Szyrocka, J., Khan, M.A., Sharif, A.: Trust in renewable energy as part of energy-saving knowledge. Energies 15, 1566 (2022). https://doi.org/10.3390/EN15041566

  9. Adefarati, T., Bansal, R.C.: Energizing renewable energy systems and distribution generation. In: Pathways to a Smarter Power System, pp. 29–65. Elsevier (2019)

    Google Scholar 

  10. Scholten, D., Bazilian, M., Overland, I., Westphal, K.: The geopolitics of renewables: new board, new game. Energy Policy, 111059 (2020). https://doi.org/10.1016/j.enpol.2019.111059

  11. Thakare, Y., Kore, S., Dharaskar, S.: Emerging technologies in conventional and nonconventional energy sources. Emerg. Technol. Sustain. Smart Energy, 1–16 (2022). https://doi.org/10.1201/B23013-1

  12. Aishwarya, P., Abhinaya, G., Alamelu, A., Jeevithaa, M., Snehaa, S., Swathi, S., Varsinee, R.: Awareness on environmental degradation by conventional energy sources - persuading the adoption of solar energy products. J. Sales Serv. Market. Res., 71–95 (2022). e-ISSN 2582-7804

    Google Scholar 

  13. Luna-Rubio, R., Trejo-Perea, M., Vargas-Vázquez, D., Ríos-Moreno, G.J.: Optimal sizing of renewable hybrids energy systems: a review of methodologies. Sol. Energy 86, 1077–1088 (2012). https://doi.org/10.1016/j.solener.2011.10.016

    Article  Google Scholar 

  14. Redko, A., Redko, O., DiPippo, R.: Hybrid systems with renewable energy sources. In: Low-Temperature Energy Systems with Applications of Renewable Energy, pp. 289–328. Elsevier (2020)

    Google Scholar 

  15. Khare, V., Nema, S., Baredar, P.: Solar-wind hybrid renewable energy system: a review. Renew. Sustain. Energy Rev. 58, 23–33 (2016)

    Article  Google Scholar 

  16. Shams, M.H., Niaz, H., Hashemi, B., Jay Liu, J., Siano, P., Anvari-Moghaddam, A.: Artificial intelligence-based prediction and analysis of the oversupply of wind and solar energy in power systems. Energy Convers. Manag. 250, 114892 (2021). https://doi.org/10.1016/J.ENCONMAN.2021.114892

    Article  Google Scholar 

  17. Zhang, L., Ling, J., Lin, M.: Artificial intelligence in renewable energy: a comprehensive bibliometric analysis. Energy Rep. 8, 14072–14088 (2022). https://doi.org/10.1016/J.EGYR.2022.10.347

    Article  Google Scholar 

  18. Zhou, Y.: Artificial intelligence in renewable systems for transformation towards intelligent buildings. Energy AI 10, 100182 (2022). https://doi.org/10.1016/J.EGYAI.2022.100182

    Article  Google Scholar 

  19. Del Ser, J., et al.: Randomization-based machine learning in renewable energy prediction problems: critical literature review, new results and perspectives. Appl. Soft Comput. 118, 108526 (2022). https://doi.org/10.1016/J.ASOC.2022.108526

    Article  Google Scholar 

  20. Chen, C., Hu, Y., Marimuthu, K., Kumar, P.M.: Artificial intelligence on economic evaluation of energy efficiency and renewable energy technologies. Sustain. Energy Technol. Assess. 47, 101358 (2021). https://doi.org/10.1016/J.SETA.2021.101358

    Article  Google Scholar 

  21. Hannan, M.A., et al.: Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals. Energy Rep. 7, 5359–5373 (2021). https://doi.org/10.1016/J.EGYR.2021.08.172

    Article  Google Scholar 

  22. Olabi, A.G., et al.: Application of artificial intelligence for prediction, optimization, and control of thermal energy storage systems. Thermal Sci. Eng. Progress 39, 101730 (2023). https://doi.org/10.1016/J.TSEP.2023.101730

  23. He, Z., Guo, W., Zhang, P.: Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods. Renew. Sustain. Energy Rev. 156, 111977 (2022). https://doi.org/10.1016/J.RSER.2021.111977

    Article  Google Scholar 

  24. Runge, J., Saloux, E.: A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system. Energy 269, 126661 (2023). https://doi.org/10.1016/J.ENERGY.2023.126661

    Article  Google Scholar 

  25. Wu, Q., Ren, H., Shi, S., Fang, C., Wan, S., Li, Q.: Analysis and prediction of industrial energy consumption behavior based on big data and artificial intelligence. Energy Rep. 9, 395–402 (2023). https://doi.org/10.1016/J.EGYR.2023.01.007

    Article  Google Scholar 

  26. Mazzeo, D., et al.: Artificial intelligence application for the performance prediction of a clean energy community. Energy 232, 120999 (2021). https://doi.org/10.1016/J.ENERGY.2021.120999

    Article  Google Scholar 

  27. Hu, Y., Li, J., Hong, M., Ren, J., Man, Y.: Industrial artificial intelligence based energy management system: integrated framework for electricity load forecasting and fault prediction. Energy 244, 123195 (2022). https://doi.org/10.1016/J.ENERGY.2022.123195

    Article  Google Scholar 

  28. Ngarambe, J., Yun, G.Y., Santamouris, M.: The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls. Energy Build 211, 109807 (2020). https://doi.org/10.1016/J.ENBUILD.2020.109807

    Article  Google Scholar 

  29. Chakraborty, D., Alam, A., Chaudhuri, S., Başağaoğlu, H., Sulbaran, T., Langar, S.: Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence. Appl. Energy 291, 116807 (2021). https://doi.org/10.1016/J.APENERGY.2021.116807

    Article  Google Scholar 

  30. Pan, Y., Jiang, J., Wang, R., Cao, H.: Advantages of support vector machine in QSPR studies for predicting auto-ignition temperatures of organic compounds. Chemom. Intell. Lab. Syst. 92, 169–178 (2008). https://doi.org/10.1016/J.CHEMOLAB.2008.03.002

    Article  Google Scholar 

  31. Fan, G.F., Yu, M., Dong, S.Q., Yeh, Y.H., Hong, W.C.: Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling. Util Policy 73, 101294 (2021). https://doi.org/10.1016/J.JUP.2021.101294

    Article  Google Scholar 

  32. Kramer, O.: K-Nearest Neighbors, pp. 13–23 (2013). https://doi.org/10.1007/978-3-642-38652-7_2

  33. Vapnik, V.: The Nature of Statistical Learning Theory (1999‏)

    Google Scholar 

  34. Suthaharan, S.: Support vector machine, pp. 207–235 (2016). https://doi.org/10.1007/978-1-4899-7641-3_9

  35. Pisner, D.A., Schnyer, D.M.: Support vector machine. In: Machine Learning: Methods and Applications to Brain Disorders, pp. 101–121 (2020). https://doi.org/10.1016/B978-0-12-815739-8.00006-7

  36. Martin, M.: On-line support vector machine regression. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 282–294. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36755-1_24

    Chapter  Google Scholar 

  37. Mailagaha Kumbure, M., Luukka, P.: A generalized fuzzy K-nearest neighbor regression model based on Minkowski distance. Granul. Comput. 7, 657–671 (2022). https://doi.org/10.1007/S41066-021-00288-W

    Article  Google Scholar 

  38. Singh, A., Yadav, A., Rana, A.: K-means with three different distance metrics. Int. J. Comput. Appl. 67, 975–8887 (2013)

    Google Scholar 

  39. Hu, C., Jain, G., Zhang, P., Schmidt, C., Gomadam, P., Gorka, T.: Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery‏. Elsevier. https://doi.org/10.1016/j.apenergy.2014.04.077

  40. Biau, G., Devroye, L., Dujmović, V., Krzyżak, A.: An affine invariant K-nearest neighbor regression estimate. J. Multivariate Anal. 112, 24–34 (2012)

    Article  MathSciNet  Google Scholar 

  41. Song, Y., Liang, J., Lu, J., Zhao, X.: An efficient instance selection algorithm for k nearest neighbor regression‏. Neurocomputing (2017). https://doi.org/10.1016/j.neucom.2017.04.018

  42. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324/METRICS

    Article  Google Scholar 

  43. Biau, G., Scornet, E.: A random forest guided tour. Test 25, 197–227 (2016). https://doi.org/10.1007/S11749-016-0481-7

    Article  MathSciNet  Google Scholar 

  44. Qi, Y.: Random forest for bioinformatics. In: Ensemble Machine Learning, pp. 307–323 (2012). https://doi.org/10.1007/978-1-4419-9326-7_11

  45. Luo, N., et al.: A three-year dataset supporting research on building energy management and occupancy analytics‏. ‏Sci. Data (2022). https://doi.org/10.1038/s41597-022-01257-x

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Authors and Affiliations

  1. Pluridisciplinary Laboratory of Research and Innovation (LPRI), EMSI of Casablanca, Casablanca, Morocco

    Asmae Chakir, Sonia Souabi & Mohamed Tabaa

Authors
  1. Asmae Chakir
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  2. Sonia Souabi
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  3. Mohamed Tabaa
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Corresponding author

Correspondence to Asmae Chakir .

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Editors and Affiliations

  1. University of Hassan II Casablanca, Casablanca, Morocco

    Mohamed Hamlich

  2. University of the Basque Country, San Sebastian, Spain

    Fadi Dornaika

  3. University of Houston, Houston, TX, USA

    Carlos Ordonez

  4. ISAE-ENSMA, Poitiers, France

    Ladjel Bellatreche

  5. University of Hassan II Casablanca, Morocco, Morocco

    Hicham Moutachaouik

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Chakir, A., Souabi, S., Tabaa, M. (2024). Machine Learning Algorithms Based for Demand-Side Energy Forecasting of an Office Building Loads. In: Hamlich, M., Dornaika, F., Ordonez, C., Bellatreche, L., Moutachaouik, H. (eds) Smart Applications and Data Analysis. SADASC 2024. Communications in Computer and Information Science, vol 2168. Springer, Cham. https://doi.org/10.1007/978-3-031-77043-2_9

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