Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Science and Its Applications

ICCSA 2019: Computational Science and Its Applications – ICCSA 2019 pp 219–230Cite as

Artificial Intelligence Techniques for Electrical Load Forecasting in Smart and Connected Communities

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11623))

Abstract

Electricity consumption has been on a rapid increase worldwide and it is a very vital component of human life in this age. Hence, reliable supply of electricity from the utility operators is a necessity. However, the constraints that electricity supplied must be the same as electricity consumed puts the burden on the utility operators to make sure that demand is equal to supply at any point in time in smart and connected communities. Load forecasting techniques, therefore, aim to resolve these challenges for the operators by providing accurate forecasts of electrical load demand. This paper reviews current and mostly used short term forecasting techniques, drawing parallels be-tween them; and highlighting their advantages and disadvantages. This paper concludes by stating that there is no one-size-fits-all technique for load forecasting problems, as appropriate techniques depend on several factors such as data size and variability and environmental variables. Different optimization techniques can be used whether to reduce errors and its variations or to speed up computational time, hence resulting in an improved model. However, it is imperative to consider the tradeoffs between each model and its different variants in the context of smart and connected communities.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Wolde-Rufael, Y.: Electricity consumption and economic growth: a time series experience for 17 African countries. Energy Policy 34(10), 1106–1114 (2006)

    Article  Google Scholar 

  2. Lu, W.-C.: Electricity consumption and economic growth: evidence from 17 Taiwanese industries. Sustainability 9(1), 1–15 (2016)

    Article  Google Scholar 

  3. Mohan, N., Soman, K.P., Sachin Kumar, S.: A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model. Appl. Energy 232, 229–244 (2018)

    Article  Google Scholar 

  4. Amara, F., et al.: Household electricity demand forecasting using adaptive conditional density estimation. Energy Build. 156, 271–280 (2017)

    Article  Google Scholar 

  5. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)

    MATH  Google Scholar 

  6. Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edn. Pearson (2009)

    Google Scholar 

  7. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory (1992)

    Google Scholar 

  8. Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)

    Article  Google Scholar 

  9. Kuster, C., Rezgui, Y., Mourshed, M.: Electrical load forecasting models: a critical systematic review. Sustain. Cities Soc. 35, 257–270 (2017)

    Article  Google Scholar 

  10. Auria, L., Moro, R.A.: Support Vector Machines (SVM) as a Technique for Solvency Analysis. DIW Berlin, German Institute for Economic Research (2008)

    Google Scholar 

  11. Zhao, H.X., Magoulés, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16(6), 3586–3592 (2012)

    Article  Google Scholar 

  12. Hong, T., Fan, S.: Probabilistic electric load forecasting: a tutorial review. Int. J. Forecast. 32(3), 914–938 (2016)

    Article  Google Scholar 

  13. Adhikari, R., Agrawal, R.K.: An introductory study on time series modeling and forecasting (2013)

    Google Scholar 

  14. Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  15. Elias, C.N., Hatziargyriou, N.D.: An annual midterm energy forecasting model using fuzzy logic. IEEE Trans. Power Syst. 24(1), 469–478 (2009)

    Article  Google Scholar 

  16. Chen, S.X., Gooi, H.B., Wang, M.Q.: Solar radiation forecast based on fuzzy logic and neural networks. Renew. Energy 60, 195–201 (2013)

    Article  Google Scholar 

  17. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: IEEE International Joint Conference (2004)

    Google Scholar 

  18. Zhu, Q.-Y., et al.: Evolutionary extreme learning machine. Pattern Recogn. 38(10), 1759–1763 (2005)

    Article  MATH  Google Scholar 

  19. Tissera, M.D., McDonnell, M.D.: Deep extreme learning machines: supervised autoencoding architecture for classification. Neurocomputing 174, 42–49 (2016)

    Article  Google Scholar 

  20. Chae, Y.T., et al.: Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build. 111, 184–194 (2016)

    Article  Google Scholar 

  21. Moazzami, M., Khodabakhshian, A., Hooshmand, R.: A new hybrid day-ahead peak load forecasting method for Iran’s National Grid. Appl. Energy 101, 489–501 (2013)

    Article  Google Scholar 

  22. Hu, R., et al.: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221, 24–31 (2017)

    Article  Google Scholar 

  23. Khwaja, A.S., et al.: Improved short-term load forecasting using bagged neural networks. Electr. Power Syst. Res. 125, 109–115 (2015)

    Article  Google Scholar 

  24. Khwaja, A.S., et al.: Boosted neural networks for improved short-term electric load forecasting. Electr. Power Syst. Res. 143, 431–437 (2017)

    Article  Google Scholar 

  25. Zhang, J., et al.: Enhancing performance of the backpropagation algorithm via sparse response regularization. Neurocomputing 153, 20–40 (2015)

    Article  Google Scholar 

  26. Ozerdem, O.C., Olaniyi, E.O., Oyedotun, O.K.: Short term load forecasting using particle swarm optimization neural network. Procedia Comput. Sci. 120, 382–393 (2017)

    Article  Google Scholar 

  27. Guo, Z., et al.: A deep learning model for short-term power load and probability density forecasting. Energy 160, 1186–1200 (2018)

    Article  Google Scholar 

  28. He, W.: Load forecasting via deep neural networks. Procedia Comput. Sci. 122, 308–314 (2017)

    Article  Google Scholar 

  29. Muralitharan, K., Sakthivel, R., Vishnuvarthan, R.: Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing 273, 199–208 (2018)

    Article  Google Scholar 

  30. Chitsaz, H., et al.: Short-term electricity load forecasting of buildings in microgrids. Energy Build. 99, 50–60 (2015)

    Article  Google Scholar 

  31. Rana, M., Koprinska, I.: Forecasting electricity load with advanced wavelet neural networks. Neurocomputing 182, 118–132 (2016)

    Article  Google Scholar 

  32. Rahman, A., Srikumar, V., Smith, A.D.: Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl. Energy 212, 372–385 (2018)

    Article  Google Scholar 

  33. Koschwitz, D., Frisch, J., van Treeck, C.: Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: a comparative study on district scale. Energy 165, 134–142 (2018)

    Article  Google Scholar 

  34. Ruiz, L.G.B., et al.: Energy consumption forecasting based on Elman neural networks with evolutive optimization. Expert Syst. Appl. 92, 380–389 (2018)

    Article  Google Scholar 

  35. Ko, C.-N., Lee, C.-M.: Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 49, 413–422 (2013)

    Article  Google Scholar 

  36. Che, J., Wang, J.: Short-term load forecasting using a kernel-based support vector regression combination model. Appl. Energy 132, 602–609 (2014)

    Article  Google Scholar 

  37. Chen, Y., Tan, H., Song, X.: Day-ahead forecasting of non-stationary electric power demand in commercial buildings: hybrid support vector regression based. Energy Procedia 105, 2101–2106 (2017)

    Article  Google Scholar 

  38. Yang, W., Kang, C., Xia, Q., et al.: Short-term probabilistic load forecasting based on statistics of probability distribution of forecasting errors. Autom. Electr. Power Syst. 30(19), 47–52 (2006)

    Google Scholar 

  39. Niu, D., Wang, Y., Wu, D.D.: Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl. 37(3), 2531–2539 (2010)

    Article  Google Scholar 

  40. Tong, C., et al.: An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. J. Parallel Distrib. Comput. 117, 267–273 (2018)

    Article  Google Scholar 

  41. Vrablecová, P., et al.: Smart grid load forecasting using online support vector regression. Comput. Electr. Eng. 65, 102–117 (2018)

    Article  Google Scholar 

  42. Li, Y., Che, J., Yang, Y.: Subsampled support vector regression ensemble for short term electric load forecasting. Energy 164, 160–170 (2018)

    Article  Google Scholar 

  43. Zheng, Y., Zhu, L., Zou, X.: Short-term load forecasting based on Gaussian wavelet SVM. Energy Procedia 12, 387–393 (2011)

    Article  Google Scholar 

  44. Zhang, X., Wang, J.: A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns. Appl. Soft Comput. 65, 478–494 (2018)

    Article  Google Scholar 

  45. Zeng, X.-Y., et al.: Triangular fuzzy series forecasting based on grey model and neural network. Appl. Math. Model. 40(3), 1717–1727 (2016)

    Article  MathSciNet  Google Scholar 

  46. Coelho, V.N., et al.: A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment. Appl. Energy 169, 567–584 (2016)

    Article  Google Scholar 

  47. Liu, N., et al.: A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids. Appl. Energy 129, 336–345 (2014)

    Article  Google Scholar 

  48. Sala-Cardoso, E., et al.: Activity-aware HVAC power demand forecasting. Energy Build. 170, 15–24 (2018)

    Article  Google Scholar 

  49. Chen, Y., et al.: Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 312, 90–106 (2018)

    Article  Google Scholar 

  50. Jiang, P., Liu, F., Song, Y.: A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting. Energy 119, 694–709 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IoT-Enabled Smart and Connected Communities (SmartCU) Research Cluster, Covenant University, Ota, Nigeria

    Victor Alagbe, Segun I. Popoola, Aderemi A. Atayero & Sanjay Misra

  2. Department of Engineering, Manchester Metropolitan University, Manchester, M1 5GD, UK

    Segun I. Popoola & Bamidele Adebisi

  3. Department of Electronic and Electrical Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

    Robert O. Abolade

Authors
  1. Victor Alagbe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Segun I. Popoola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aderemi A. Atayero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bamidele Adebisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert O. Abolade
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sanjay Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Segun I. Popoola .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg , Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alagbe, V., Popoola, S.I., Atayero, A.A., Adebisi, B., Abolade, R.O., Misra, S. (2019). Artificial Intelligence Techniques for Electrical Load Forecasting in Smart and Connected Communities. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24308-1_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24307-4

  • Online ISBN: 978-3-030-24308-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation