Skip to main content
SpringerLink
Log in

Deep Learning Techniques for Load Forecasting

  • Chapter
  • First Online:
Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 956))

  • 225 Accesses

Abstract

Electricity load dominates energy consumption and greenhouse gas emissions. There are increasing concerns about climate change and the need to minimize energy consumption and enhance energy performance. Energy management, optimization, and planning all depend on forecasting load energy consumption. The data-driven approaches are the most popular approaches to energy forecasting. Deep learning techniques are a new category of data-driven models that have emerged in the recent years. They offer improved capabilities in managing big data, attribute extraction characteristics, and a better ability to model nonlinear phenomena. This paper examines the effectiveness and potential of deep learning-based approaches for load energy forecasting. This paper begins with a literature survey, tracked through an outline of deep learning-based concepts, methodologies, and examples. Following that, the current trends in published research were examined and how deep learning-based approaches may be utilized for forecasting and feature extraction. The study finishes with an analysis of current problems and recommendations for further research.

P. Gupta and A. Tomar: These authors contributed equally to this work.

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

Access this chapter

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Kumar R, Aggarwal R, Sharma J (2013) Energy analysis of a building using artificial neural network: a review. Energy Build 65:352–358

    Article  Google Scholar 

  4. Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102–109

    Article  Google Scholar 

  5. Wang Z, Srinivasan RS (2017) A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew Sustain Energy Rev 75:796–808

    Article  Google Scholar 

  6. Wang Z, Srinivasan RS (2015) A review of artificial intelligence based building energy prediction with a focus on ensemble prediction models. In: 2015 Winter simulation conference (WSC). IEEE, New York, pp 3438–3448

    Google Scholar 

  7. Deb C, Zhang F, Yang J, Lee SE, Shah KW (2017) A review on time series forecasting techniques for building energy consumption. Renew Sustain Energy Rev 74:902–924

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Wei Y, Zhang X, Shi Y, Xia L, Pan S, Wu J, Han M, Zhao X (2018) A review of data-driven approaches for prediction and classification of building energy consumption. Renew Sustain Energy Rev 82:1027–1047

    Article  Google Scholar 

  10. Ahmad T, Chen H, Guo Y, Wang J (2018) A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: a review. Energy Build 165:301–320

    Article  Google Scholar 

  11. Bourdeau M, Qiang Zhai X, Nefzaoui E, Guo X, Chatellier P (2019) Modeling and forecasting building energy consumption: a review of data-driven techniques. Sustain Cities Soc 48:101533

    Article  Google Scholar 

  12. Mohandes SR, Zhang X, Mahdiyar A (2019) A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing 340:55–75

    Article  Google Scholar 

  13. Runge J, Zmeureanu R (2019) Forecasting energy use in buildings using artificial neural networks: a review. Energies 12(17):3254

    Article  Google Scholar 

  14. Wang H, Lei Z, Zhang X, Zhou B, Peng J (2019) A review of deep learning for renewable energy forecasting. Energy Convers Manage 198:111799

    Article  Google Scholar 

  15. Aslam Z, Javaid N, Ahmad A, Ahmed A, Gulfam SM (2020) A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids. Energies 13(21):5599

    Article  Google Scholar 

  16. Marcjasz G (2020) Forecasting electricity prices using deep neural networks: a robust hyper-parameter selection scheme. Energies 13(18):4605

    Article  Google Scholar 

  17. Tao Q, Liu F, Li Y, Sidorov D (2019) Air pollution forecasting using a deep learning model based on 1d convnets and bidirectional GRU. IEEE Access 7:76690–76698

    Article  Google Scholar 

  18. Runge J, Zmeureanu R (2021) A review of deep learning techniques for forecasting energy use in buildings. Energies 14(3):608

    Article  Google Scholar 

  19. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  20. Wang H, Raj B (2017) On the origin of deep learning. arXiv preprint arXiv:1702.07800

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

    Article  Google Scholar 

  22. Fan C, Xiao F, Zhao Y (2017) A short-term building cooling load prediction method using deep learning algorithms. Appl energy 195:222–233

    Article  Google Scholar 

  23. Fan C, Wang J, Gang W, Li S (2019) Assessment of deep recurrent neural network-based strategies for short-term building energy predictions. Appl Energy 236:700–710

    Article  Google Scholar 

  24. Mishra S, Palanisamy P (2018) Multi-time-horizon solar forecasting using recurrent neural network. In: 2018 IEEE energy conversion congress and exposition (ECCE). IEEE, New York, pp 18–24

    Google Scholar 

  25. Xiaoqiao H, Zhang C, Li Q, Yonghang T, Gao B, Shi J (2020) A comparison of hour-ahead solar irradiance forecasting models based on LSTM network. Math Prob Eng 2020:1–15

    Google Scholar 

  26. Srivastava S, Lessmann S (2018) A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Solar Energy 162:232–247

    Article  Google Scholar 

  27. Son M, Moon J, Jung S, Hwang E (2018) A short-term load forecasting scheme based on auto-encoder and random forest. In: International conference on applied physics, system science and computers. Springer, Berlin, pp 138–144

    Google Scholar 

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

    Article  Google Scholar 

  29. Kim J, Moon J, Hwang E, Kang P (2019) Recurrent inception convolution neural network for multi short-term load forecasting. Energy Build 194:328–341

    Article  Google Scholar 

  30. Kim T-Y, Cho S-B (2019) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81

    Article  Google Scholar 

  31. Somu N, Gauthama Raman MR, Ramamritham K (2020) A hybrid model for building energy consumption forecasting using long short term memory networks. Appl Energy 261:114131

    Article  Google Scholar 

  32. Shi Z, Li H, Cao Q, Ren H, Fan B (2020) An image mosaic method based on convolutional neural network semantic features extraction. J Sign Process Syst 92(4):435–444

    Article  Google Scholar 

  33. He W (2017) Load forecasting via deep neural networks. Proc Comput Sci 122:308–314

    Article  Google Scholar 

  34. Wang J, Chen X, Zhang F, Chen F, Xin Y (2021) Building load forecasting using deep neural network with efficient feature fusion. J Mod Power Syst Clean Energy 9(1):160–169

    Article  Google Scholar 

  35. Kong Z, Zhang C, Lv H, Xiong F, Fu Z (2020) Multimodal feature extraction and fusion deep neural networks for short-term load forecasting. IEEE Access 8:185373–185383

    Article  Google Scholar 

  36. Chitalia G, Pipattanasomporn M, Garg V, Rahman S (2020) Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. Appl Energy 278:115410

    Article  Google Scholar 

  37. Zhang G, Tian C, Li C, Zhang JJ, Zuo W (2020) Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature. Energy 201:117531

    Article  Google Scholar 

  38. Fan C, Sun Y, Zhao Y, Song M, Wang J (2019) Deep learning-based feature engineering methods for improved building energy prediction. Appl energy 240:35–45

    Article  Google Scholar 

  39. Laib O, Khadir MT, Mihaylova L (2019) Toward efficient energy systems based on natural gas consumption prediction with LSTM recurrent neural networks. Energy 177:530–542

    Article  Google Scholar 

  40. Wang Z, Hong T, Piette MA (2020) Building thermal load prediction through shallow machine learning and deep learning. Appl Energy 263:114683

    Article  Google Scholar 

  41. Yang J, Tan KK, Santamouris M, Lee SE (2019) Building energy consumption raw data forecasting using data cleaning and deep recurrent neural networks. Buildings 9(9):204

    Article  Google Scholar 

  42. Marino DL, Amarasinghe K, Manic M (2016) Building energy load forecasting using deep neural networks. In: IECON 2016–42nd annual conference of the IEEE Industrial Electronics Society. IEEE, New York, pp 7046–7051

    Google Scholar 

  43. Nichiforov C, Stamatescu G, Stamatescu I, Calofir V, Fagarasan I, Iliescu SS (2018) Deep learning techniques for load forecasting in large commercial buildings. In: 2018 22nd international conference on system theory, control and computing (ICSTCC). IEEE, New York, pp 492–497

    Google Scholar 

  44. Su H, Zio E, Zhang J, Xu M, Li X, Zhang Z (2019) A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced deep-RNN model. Energy 178:585–597

    Article  Google Scholar 

  45. Xue P, Jiang Y, Zhou Z, Chen X, Fang X, Liu J (2019) Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms. Energy 188:116085

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, Delhi, India

    Neeraj & Pankaj Gupta

  2. Department of Instrumentation and Control Engineering, Netaji Subhas University of Technology, New Delhi, Delhi, India

    Anuradha Tomar

Authors
  1. Neeraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pankaj Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anuradha Tomar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neeraj .

Editor information

Editors and Affiliations

  1. ICE Department, Netaji Subhas University of Technology, New Delhi, Delhi, India

    Anuradha Tomar

  2. Netaji Subhas University of Technology, New Delhi, India

    Prerna Gaur

  3. Department of Electrical Engineering, Technical University of Denmark, Lyngby, Denmark

    Xiaolong Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Neeraj, Gupta, P., Tomar, A. (2023). Deep Learning Techniques for Load Forecasting. In: Tomar, A., Gaur, P., Jin, X. (eds) Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting. Lecture Notes in Electrical Engineering, vol 956. Springer, Singapore. https://doi.org/10.1007/978-981-19-6490-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-6490-9_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6489-3

  • Online ISBN: 978-981-19-6490-9

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation