Skip to main content
Springer Nature Link
Log in

Predicting Energy Consumption Data Using Deep Learning: An LSTM Approach

  • Conference paper
  • First Online:
Intelligent Systems (BRACIS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15413))

Included in the following conference series:

  • 4 Accesses

Abstract

Nowadays, most of the energy produced globally comes from fossil fuels. However, this type of energy generation harms the environment by emitting various toxic residues in natural bodies. In recent years, an area that has gained strength is producing and consuming clean energy. One that stands out is solar energy production because it is easy to implement and relatively cheap compared to other clean energy production. Therefore, several projects were created focusing on generating energy from sunlight, and using it. Due to this growing number of enterprises in the area, the creation of applications to help manage production and its use has increased. The use of Deep Learning techniques to help this industry has also gained strength; predictive models for energy consumption have been widely studied to help enterprises in future planning. In this work, we developed a deep learning model using Long Short-Term Memory (LSTM) capable of predicting energy consumption from solar power plant data, using the data to train the model and make inferences in the future. We explored configuration combinations such as data filtering with smoothing techniques, model hyperparameters, number of layers, number of neurons, and optimal prediction horizon. The achieved results demonstrate the validity and effectiveness of the implemented methodology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abd El-Aziz, R.M.: Renewable power source energy consumption by hybrid machine learning model. Alex. Eng. J. 61(12), 9447–9455 (2022)

    Article  MATH  Google Scholar 

  2. Amasyali, K., El-Gohary, N.M.: A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205 (2018)

    Article  MATH  Google Scholar 

  3. Bertolini, M., Mezzogori, D., Neroni, M., Zammori, F.: Machine learning for industrial applications: a comprehensive literature review. Expert Syst. Appl. 175, 114820 (2021)

    Article  MATH  Google Scholar 

  4. Box, G.E., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65(332), 1509–1526 (1970)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chan, S.L., Lu, Y., Wang, Y.: Data-driven cost estimation for additive manufacturing in cybermanufacturing. J. Manuf. Syst. 46, 115–126 (2018). https://doi.org/10.1016/j.jmsy.2017.12.001, https://www.sciencedirect.com/science/article/pii/S0278612517301577

  6. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)

    Google Scholar 

  7. Dincer, I.: Environmental impacts of energy. Energy Policy 27(14), 845–854 (1999)

    Article  MATH  Google Scholar 

  8. Du, S., Li, T., Yang, Y., Horng, S.J.: Multivariate time series forecasting via attention-based encoder-decoder framework. Neurocomputing 388, 269–279 (2020)

    Article  MATH  Google Scholar 

  9. Ekonomou, L.: Greek long-term energy consumption prediction using artificial neural networks. Energy 35(2), 512–517 (2010)

    Article  MATH  Google Scholar 

  10. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  MATH  Google Scholar 

  11. Hu, H., Wang, L., Peng, L., Zeng, Y.R.: Effective energy consumption forecasting using enhanced bagged echo state network. Energy 193, 116778 (2020)

    Article  MATH  Google Scholar 

  12. Iqbal, R., Maniak, T., Doctor, F., Karyotis, C.: Fault detection and isolation in industrial processes using deep learning approaches. IEEE Trans. Industr. Inf. 15(5), 3077–3084 (2019)

    Article  Google Scholar 

  13. Irena, A.: Renewable capacity highlights. Proc. Int. Renew. Energy Agency (IRENA), 1–8 (2020)

    Google Scholar 

  14. Jefferson, M.: World energy outlook to 2100. In: World Petroleum Congress, pp. WPC–26015. WPC (1994)

    Google Scholar 

  15. Jennings, C., Wu, D., Terpenny, J.: Forecasting obsolescence risk and product life cycle with machine learning. IEEE Trans. Compon. Packag. Manuf. Technol. 6(9), 1428–1439 (2016). https://doi.org/10.1109/TCPMT.2016.2589206

    Article  MATH  Google Scholar 

  16. Ji, S., Wang, X., Zhao, W., Guo, D.: An application of a three-stage XGBoost-based model to sales forecasting of a cross-border e-commerce enterprise. Math. Probl. Eng. 2019 (2019)

    Google Scholar 

  17. Lima, M.A.F., Carvalho, P.C., Fernández-Ramírez, L.M., Braga, A.P.: Improving solar forecasting using deep learning and portfolio theory integration. Energy 195, 117016 (2020)

    Article  Google Scholar 

  18. Maggipinto, M., Terzi, M., Masiero, C., Beghi, A., Susto, G.A.: A computer vision-inspired deep learning architecture for virtual metrology modeling with 2-dimensional data. IEEE Trans. Semicond. Manuf. 31(3), 376–384 (2018). https://doi.org/10.1109/TSM.2018.2849206

    Article  MATH  Google Scholar 

  19. Mahjoub, S., Chrifi-Alaoui, L., Marhic, B., Delahoche, L.: Predicting energy consumption using LSTM, multi-layer GRU and drop-GRU neural networks. Sensors 22(11), 4062 (2022)

    Article  MATH  Google Scholar 

  20. Mezzogori, D., Zammori, F.: An entity embeddings deep learning approach for demand forecast of highly differentiated products. Procedia Manuf. 39, 1793–1800 (2019). https://doi.org/10.1016/j.promfg.2020.01.260, https://www.sciencedirect.com/science/article/pii/S2351978920303243, 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, 9–14 August 2019|Chicago, Illinois (USA)

  21. Mirzaliev, S., Sharipov, K.: A review of energy efficient fluid power systems: fluid power impact on energy, emissions and economics, vol. 30 (2020)

    Google Scholar 

  22. Oh, Y., Ransikarbum, K., Busogi, M., Kwon, D., Kim, N.: Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line. Reliab. Eng. Syst. Saf. 184, 202–212 (2019). https://doi.org/10.1016/j.ress.2018.03.020, https://www.sciencedirect.com/science/article/pii/S0951832017303861, Impact of Prognostics and Health Management in Systems Reliability and Maintenance Planning

  23. Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., Ajayi, S.: Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. J. Build. Eng. 45, 103406 (2022)

    Article  Google Scholar 

  24. Rabaia, M.K.H., et al.: Environmental impacts of solar energy systems: a review. Sci. Total Environ. 754, 141989 (2021)

    Article  Google Scholar 

  25. Scime, L., Beuth, J.: Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Addit. Manuf. 25, 151–165 (2019). https://doi.org/10.1016/j.addma.2018.11.010, https://www.sciencedirect.com/science/article/pii/S2214860418306869

  26. Shamshirband, S., Rabczuk, T., Chau, K.W.: A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access 7, 164650–164666 (2019)

    Article  Google Scholar 

  27. Taylor, S.J., Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37–45 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  28. Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., Rajagopal, R.: Neuralprophet: Explainable forecasting at scale. arxiv 2021. arXiv preprint arXiv:2111.15397

  29. Wang, J.Q., Du, Y., Wang, J.: LSTM based long-term energy consumption prediction with periodicity. Energy 197, 117197 (2020)

    Article  MATH  Google Scholar 

  30. Wibawa, A.P., Utama, A.B.P., Elmunsyah, H., Pujianto, U., Dwiyanto, F.A., Hernandez, L.: Time-series analysis with smoothed convolutional neural network. J. Big Data 9(1), 44 (2022)

    Article  Google Scholar 

  31. Wilberforce, T., Baroutaji, A., El Hassan, Z., Thompson, J., Soudan, B., Olabi, A.G.: Prospects and challenges of concentrated solar photovoltaics and enhanced geothermal energy technologies. Sci. Total Environ. 659, 851–861 (2019)

    Article  Google Scholar 

  32. Xiao, Z.: Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning. Energy Build. 258, 111832 (2022)

    Article  MATH  Google Scholar 

  33. Yagli, G.M., Yang, D., Srinivasan, D.: Automatic hourly solar forecasting using machine learning models. Renew. Sustain. Energy Rev. 105, 487–498 (2019)

    Article  Google Scholar 

Download references

Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Also Pedro Pedrosa Rebouças Filho acknowledges the sponsorship from the Brazilian National Council for Research and Development (CNPq) via Grant 301455/2022-8.

Author information

Authors and Affiliations

  1. Laboratory for Processing Images, Signals and Computer Science (LAPISCO), Fortaleza, Ceará, Brazil

    Julio Macedo Chaves, Elene Firmeza Ohata, Matheus Araujo dos Santos, José Daniel de Alencar Santos & Pedro Pedrosa Rebouças Filho

  2. Federal Institute of Education, Science and Technology of Ceará (IFCE), Fortaleza, Ceará, Brazil

    Julio Macedo Chaves, José Daniel de Alencar Santos & Pedro Pedrosa Rebouças Filho

  3. Federal University of Ceará (UFC), Fortaleza, Ceará, Brazil

    Elene Firmeza Ohata, Matheus Araujo dos Santos & Pedro Pedrosa Rebouças Filho

  4. Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil

    Matheus Jardim Bernardes & Daniel Seleme Dora

Authors
  1. Julio Macedo Chaves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elene Firmeza Ohata
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matheus Araujo dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Daniel de Alencar Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matheus Jardim Bernardes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel Seleme Dora
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pedro Pedrosa Rebouças Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julio Macedo Chaves .

Editor information

Editors and Affiliations

  1. Universidade Federal Fluminense, Niterói, Brazil

    Aline Paes

  2. Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil

    Filipe A. N. Verri

Ethics declarations

Disclosure of Interests

The authors have no competing interests to declare that are relevant to the content of this article.

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaves, J.M. et al. (2025). Predicting Energy Consumption Data Using Deep Learning: An LSTM Approach. In: Paes, A., Verri, F.A.N. (eds) Intelligent Systems. BRACIS 2024. Lecture Notes in Computer Science(), vol 15413. Springer, Cham. https://doi.org/10.1007/978-3-031-79032-4_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-79032-4_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-79031-7

  • Online ISBN: 978-3-031-79032-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics