Skip to main content
SpringerLink
Log in

Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Load Forecasting in Power System

  • Chapter
  • First Online:
Application of Machine Learning and Deep Learning Methods to Power System Problems

Part of the book series: Power Systems ((POWSYS))

Abstract

Nowadays, the increment of energy demand in the world as well as the development of smart grids and the combination of different types of energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of renewable energies have made electricity networks operating in more complex and uncertain conditions. Also, consumers and especially users of sensitive load tend to have access to a reliable and sustainable power supply. Therefore, power producers need a variety of long- and short-term planning methodologies for attaining to sustainable investment, production, and operation. Analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze the behavior of these systems, advanced metering, and condition monitoring devices and systems are utilized, which generate a huge amount of data. Evaluation of these data is approximately impossible by conventional or numerical methods, and it requires powerful data mining procedures. Regression, classification, and clustering applications of machine learning and deep learning methods are powerful tools to use for dealing with such issues. These procedures can be utilized for load/demand forecasting, renewable energy generation forecasting, demand response evaluation, and power system analysis. Understanding the problem and functioning of each learning methods is, therefore, one of the most important issues in the application of such approaches to solve power system problems. Accordingly, in this chapter, the authors will introduce and discuss selected applications of machine learning and deep learning based on their learning, structure, mode of operation, and application in the load forecasting of power systems. Literature review on machine learning and deep learning applications in load forecasting will be presented in this chapter.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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

Abbreviations

ACE:

Average coverage error

ANN:

Artificial neural network

APE:

Absolute percentage error

AR:

Auto-regressive

ARIMA:

Auto-regressive integrated moving average

ARMA:

Auto-regressive moving average

BGA:

Binary genetic algorithm

BPNN:

Back-propagation neural network

CNN:

Convolution neural network

CV:

Coefficient of variance

CWC:

Coverage width-based criterion

DA:

Direction accuracy

DAME:

Daily absolute maximum error

DBN:

Deep belief network

DBN:

Deep neural network

DC:

Directional change

DMD:

Dynamic mode decomposition

EMAE:

Envelope-weighted mean absolute error

EMD:

Empirical mode decomposition

ENN:

Elman neural network

ESN:

Echo state network

FCRBM:

Factored conditional restricted Boltzmann machine

FFN:

Feed-forward neural network

GB:

Gradient boosting

GBA:

Gradient boosting machine

GELM :

Generalized extreme learning machine

GRU:

Gated recurrent unit

GWO:

Gray wolf optimizer

HR:

Heat rate

IA:

Index of agreement

IWNN:

Improved wavelet neural network

LSTM:

Long short-term memory

MAAPE:

Mean arctangent absolute percentage error

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MedAE:

Median absolute error

MFFNN:

Multilayer feed-forward neural network

MLP:

Multilayer perceptron

MLR:

Multiple linear regression

MOD:

Mean outside distance

MWPI:

Mean width of prediction interval

nMAE:

Normalized mean absolute error

NRMSE:

Normalized root mean squared error

NYISO:

New York independent system operator

PCR:

Principal component regression

PICP:

Prediction interval coverage probability

PJM:

Pennsylvania New Jersey Maryland

PMSE:

Prognostication mean square error

QRF:

Quantile regression forest

RBFNN:

Radial basis function neural network

RF:

Random forest

RMSE:

Root mean square error

RMSLE:

Root mean square logarithmic error

RNN:

Recurrent neural network

RVM:

Relevance vector machine

VMD:

Variational mode decomposition

WMAE:

Weighted mean absolute error

WNN:

Wavelet neural network

WOA:

Whale optimization algorithm

WT:

Wavelet transform

References

  1. A. H. Vahabie, M. M. R. Yousefi, B. N. Araabi, C. Lucas, and S. Barghinia, Combination of singular spectrum analysis and autoregressive model for short term load forecasting. 2007 IEEE Lausanne Power Tech, (2007), pp. 1090–1093

    Google Scholar 

  2. S.-J. Huang, K.-R. Shih, Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Trans. Power Syst. 18(2), 673–679 (2003)

    Article  Google Scholar 

  3. X. Wang and Y. Liu, ARIMA time series application to employment forecasting. In 2009 4th International Conference on Computer Science & Education, (2009), pp. 1124–1127

    Google Scholar 

  4. V. Debusschere, S. Bacha, One week hourly electricity load forecasting using neuro-fuzzy and seasonal ARIMA models. IFAC Proceedings Volumes 45(21), 97–102 (2012)

    Article  Google Scholar 

  5. K.-B. Song, Y.-S. Baek, D.H. Hong, G. Jang, Short-term load forecasting for the holidays using fuzzy linear regression method. IEEE Trans. Power Syst. 20(1), 96–101 (2005)

    Article  Google Scholar 

  6. N. Amral, C. S. Ozveren, and D. King, Short term load forecasting using multiple linear regression. In 2007 42nd International universities power engineering conference, (2007), pp. 1192–1198

    Google Scholar 

  7. J.W. Taylor, Short-term load forecasting with exponentially weighted methods. IEEE Trans. Power Syst. 27(1), 458–464 (Feb. 2012). https://doi.org/10.1109/TPWRS.2011.2161780

    Article  Google Scholar 

  8. N. Elamin, M. Fukushige, Modeling and forecasting hourly electricity demand by SARIMAX with interactions. Energy 165, 257–268 (2018)

    Article  Google Scholar 

  9. H. Takeda, Y. Tamura, S. Sato, Using the ensemble Kalman filter for electricity load forecasting and analysis. Energy 104, 184–198 (2016)

    Article  Google Scholar 

  10. A. Heydari, M.M. Nezhad, E. Pirshayan, D.A. Garcia, F. Keynia, L. De Santoli, Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm. Appl. Energy 277, 115503 (2020)

    Article  Google Scholar 

  11. A. Rafati, M. Joorabian, and E. Mashhour, An efficient hour-ahead electrical load forecasting method based on innovative features. Energy, p. 117511, 2020

    Google Scholar 

  12. Z. Wen, L. Xie, Q. Fan, H. Feng, Long term electric load forecasting based on TS-type recurrent fuzzy neural network model. Electr. Pow. Syst. Res. 179, 106106 (2020)

    Article  Google Scholar 

  13. A. Ganguly, K. Goswami, A. Mukherjee, and A. K. Sil, Short-term load forecasting for peak load reduction using artificial neural network technique. In Advances in Computer, Communication and Control, Springer, 2019, pp. 551–559

    Google Scholar 

  14. E. Zhao, Z. Zhang, and N. Bohlooli, Cost and load forecasting by an integrated algorithm in intelligent electricity supply network. Sustainable Cities and Society, p. 102243, 2020

    Google Scholar 

  15. C. Feng, J. Zhang, Assessment of aggregation strategies for machine-learning based short-term load forecasting. Electr. Pow. Syst. Res. 184, 106304 (2020)

    Article  Google Scholar 

  16. R.-J. Wai, Y.-C. Huang, Y.-C. Chen, Y.-W. Lin, Performance comparisons of intelligent load forecasting structures and its application to energy-saving load regulation. Soft. Comput. 17(10), 1797–1815 (2013)

    Article  Google Scholar 

  17. Z. Deng, B. Wang, Y. Xu, T. Xu, C. Liu, Z. Zhu, Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting. IEEE Access 7, 88058–88071 (2019)

    Article  Google Scholar 

  18. C. Sun, J. Song, L. Li, P. Ju, Implementation of hybrid short-term load forecasting system with analysis of temperature sensitivities. Soft. Comput. 12(7), 633–638 (2008)

    Article  Google Scholar 

  19. M. Barman, N.B. Dev Choudhury, Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept. Energy 174, 886–896 (2019). https://doi.org/10.1016/j.energy.2019.03.010

    Article  Google Scholar 

  20. M. Barman and N. B. D. Choudhury, A similarity based hybrid GWO-SVM method of power system load forecasting for regional special event days in anomalous load situations in Assam, India. Sustainable Cities and Society, p. 102311, 2020

    Google Scholar 

  21. Y. Hu et al., Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process. Energy 170, 1215–1227 (2019)

    Article  Google Scholar 

  22. A.T. Eseye, M. Lehtonen, T. Tukia, S. Uimonen, R.J. Millar, Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems. IEEE Access 7, 91463–91475 (2019)

    Article  Google Scholar 

  23. L. Limei and H. Xuan, Study of electricity load forecasting based on multiple kernels learning and weighted support vector regression machine. In 2017 29th Chinese control and decision conference (CCDC), (2017), pp. 1421–1424

    Google Scholar 

  24. P. Gangwar, A. Mallick, S. Chakrabarti, S.N. Singh, Short-term forecasting-based network reconfiguration for unbalanced distribution systems with distributed generators. IEEE Trans. Indust. Inform 16(7), 4378–4389 (2019)

    Article  Google Scholar 

  25. T. Ouyang, Y. He, H. Li, Z. Sun, S. Baek, Modeling and forecasting short-term power load with copula model and deep belief network. IEEE Trans. Emerging Top. Comput. Intelligence 3(2), 127–136 (2019)

    Article  Google Scholar 

  26. M. Rafiei, T. Niknam, J. Aghaei, M. Shafie-Khah, J.P.S. Catalão, Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine. IEEE Trans. Smart Grid 9(6), 6961–6971 (2018)

    Article  Google Scholar 

  27. X. Tang, Y. Dai, T. Wang, Y. Chen, Short-term power load forecasting based on multi-layer bidirectional recurrent neural network. IET Generation, Transmission & Distribution 13(17), 3847–3854 (2019)

    Article  Google Scholar 

  28. M. Alipour, J. Aghaei, M. Norouzi, T. Niknam, S. Hashemi, and M. Lehtonen, A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration. Energy, p. 118106, 2020

    Google Scholar 

  29. H.-A. Li et al., Combined forecasting model of cloud computing resource load for energy-efficient IoT system. IEEE Access 7, 149542–149553 (2019)

    Article  Google Scholar 

  30. J. Ding, M. Wang, Z. Ping, D. Fu, and V. S. Vassiliadis, An integrated method based on relevance vector machine for short-term load forecasting. Eur. J. Oper. Res. (2020)

    Google Scholar 

  31. M. El-Hendawi, Z. Wang, An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting. Electr. Pow. Syst. Res. 182, 106265 (2020)

    Article  Google Scholar 

  32. J. Moon, S. Jung, J. Rew, S. Rho, and E. Hwang, Combination of short-term load forecasting models based on a stacking ensemble approach. Energy and Buildings, p. 109921, 2020

    Google Scholar 

  33. A.J. Amorim, T.A. Abreu, M.S. Tonelli-Neto, C.R. Minussi, A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training. Electr. Pow. Syst. Res. 179, 106096 (2020)

    Article  Google Scholar 

  34. X. Kong, C. Li, C. Wang, Y. Zhang, J. Zhang, Short-term electrical load forecasting based on error correction using dynamic mode decomposition. Appl. Energy 261, 114368 (2020)

    Article  Google Scholar 

  35. G.T. Ribeiro, V.C. Mariani, L. dos Santos Coelho, Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting. Eng. Appl. Artif. Intel. 82, 272–281 (2019)

    Article  Google Scholar 

  36. R. Wang, J. Wang, Y. Xu, A novel combined model based on hybrid optimization algorithm for electrical load forecasting. Appl. Soft Comput. 82, 105548 (2019)

    Article  Google Scholar 

  37. M. Bessani, J.A.D. Massignan, T.M.O. Santos, J.B.A. London Jr., C.D. Maciel, Multiple households very short-term load forecasting using bayesian networks. Electr. Pow. Syst. Res. 189, 106733 (2020)

    Article  Google Scholar 

  38. M. Mansoor, F. Grimaccia, S. Leva, and M. Mussetta, Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs. Mathematics and Computers in Simulation, (2020)

    Google Scholar 

  39. G. Chitalia, M. Pipattanasomporn, V. Garg, S. Rahman, Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. Appl. Energy 278, 115410 (2020). https://doi.org/10.1016/j.apenergy.2020.115410

    Article  Google Scholar 

  40. B. Dietrich, J. Walther, M. Weigold, E. Abele, Machine learning based very short term load forecasting of machine tools. Appl. Energy 276, 115440 (2020)

    Article  Google Scholar 

  41. Q. Huang, J. Li, and M. Zhu, An improved convolutional neural network with load range discretization for probabilistic load forecasting. Energy, p. 117902, 2020

    Google Scholar 

  42. S. Ma, A hybrid deep meta-ensemble networks with application in electric utility industry load forecasting. Inform. Sci. 544, 183–196

    Google Scholar 

  43. G. Hafeez, K.S. Alimgeer, I. Khan, Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Appl. Energy 269, 114915 (2020)

    Article  Google Scholar 

  44. Y. Yang, W. Hong, S. Li, Deep ensemble learning based probabilistic load forecasting in smart grids. Energy 189, 116324 (2019)

    Article  Google Scholar 

  45. N. Zhang, Z. Li, X. Zou, S.M. Quiring, Comparison of three short-term load forecast models in Southern California. Energy 189, 116358 (2019)

    Article  Google Scholar 

  46. M. Malekizadeh, H. Karami, M. Karimi, A. Moshari, M.J. Sanjari, Short-term load forecast using ensemble neuro-fuzzy model. Energy 196, 117127 (2020)

    Article  Google Scholar 

  47. F. He, J. Zhou, L. Mo, K. Feng, G. Liu, Z. He, Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest. Appl. Energy 262, 114396 (2020)

    Article  Google Scholar 

  48. C. Fan, C. Ding, J. Zheng, L. Xiao, Z. Ai, Empirical mode decomposition based multi-objective deep belief network for short-term power load forecasting. Neurocomputing 388, 110–123 (2020)

    Article  Google Scholar 

  49. J. Bedi, D. Toshniwal, Energy load time-series forecast using decomposition and autoencoder integrated memory network. Appl. Soft Comput. 93, 106390 (2020). https://doi.org/10.1016/j.asoc.2020.106390

    Article  Google Scholar 

  50. Y. Chu et al., Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms. Energ. Buildings 225, 110343 (2020)

    Article  Google Scholar 

  51. X. Ma and Y. Dong, An estimating combination method for interval forecasting of electrical load time series. Expert Systems with Applications, p. 113498, 2020

    Google Scholar 

  52. K. Xie, H. Yi, G. Hu, L. Li, and Z. Fan, Short-term power load forecasting based on elman neural network with particle swarm optimization. Neurocomputing, (2019)

    Google Scholar 

  53. P. Singh, P. Dwivedi, V. Kant, A hybrid method based on neural network and improved environmental adaptation method using controlled Gaussian mutation with real parameter for short-term load forecasting. Energy 174, 460–477 (2019)

    Article  Google Scholar 

  54. S. Maldonado, A. González, S. Crone, Automatic time series analysis for electric load forecasting via support vector regression. Appl. Soft Comput. 83, 105616 (2019)

    Article  Google Scholar 

  55. G. Sideratos, A. Ikonomopoulos, N.D. Hatziargyriou, A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks. Electr. Pow. Syst. Res. 178, 106025 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  57. D. Sakurai, Y. Fukuyama, T. Iizaka, T. Matsui, Daily peak load forecasting by artificial neural network using differential evolutionary particle swarm optimization considering outliers. IFAC-PapersOnLine 52(4), 389–394 (2019)

    Article  Google Scholar 

  58. P. Singh, P. Dwivedi, A novel hybrid model based on neural network and multi-objective optimization for effective load forecast. Energy 182, 606–622 (2019)

    Article  Google Scholar 

  59. A. Moradzadeh, S. Zakeri, M. Shoaran, B. Mohammadi-Ivatloo, F. Mohamamdi, Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms. Sustainability (Switzerland) 12(17), 7076 (Aug. 2020). https://doi.org/10.3390/su12177076

    Article  Google Scholar 

  60. M. Mehrzadi et al., A deep learning method for short-term dynamic positioning load forecasting in maritime microgrids. Applied Sciences 10(14), 4889 (2020)

    Article  Google Scholar 

  61. S. Wang, S. Wang, D. Wang, Combined probability density model for medium term load forecasting based on quantile regression and kernel density estimation. Energy Procedia 158, 6446–6451 (2019)

    Article  Google Scholar 

  62. M. Talaat, M.A. Farahat, N. Mansour, A.Y. Hatata, Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach. Energy 196, 117087 (2020)

    Article  Google Scholar 

  63. S.-M. Jung, S. Park, S.-W. Jung, E. Hwang, Monthly electric load forecasting using transfer learning for Smart Cities. Sustainability 12(16), 6364 (2020)

    Article  Google Scholar 

  64. J. Bedi, D. Toshniwal, Empirical mode decomposition based deep learning for electricity demand forecasting. IEEE Access 6, 49144–49156 (2018)

    Article  Google Scholar 

  65. A.I. Almazrouee, A.M. Almeshal, A.S. Almutairi, M.R. Alenezi, S.N. Alhajeri, Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt–Winters Models. Applied Sciences 10(16), 5627 (2020)

    Article  Google Scholar 

  66. M. Dong, J. Shi, Q. Shi, Multi-year long-term load forecast for area distribution feeders based on selective sequence learning. Energy 206, 118209 (2020)

    Article  Google Scholar 

  67. M.-R. Kazemzadeh, A. Amjadian, and T. Amraee, A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting. Energy, p. 117948, 2020

    Google Scholar 

  68. D.A.G. Vieira, B.E. Silva, T.V. Menezes, A.C. Lisboa, Large scale spatial electric load forecasting framework based on spatial convolution. International Journal of Electrical Power & Energy Systems 117, 105582 (2020)

    Article  Google Scholar 

  69. S. Kumar, S.K. Pal, R.P. Singh, A novel method based on extreme learning machine to predict heating and cooling load through design and structural attributes. Energ. Buildings 176, 275–286 (2018). https://doi.org/10.1016/j.enbuild.2018.06.056

    Article  Google Scholar 

  70. A. Mansour-Saatloo, A. Moradzadeh, B. Mohammadi-Ivatloo, A. Ahmadian, A. Elkamel, Machine learning based PEVs load extraction and analysis. Electronics (Switzerland) 9(7), 1–15 (Jul. 2020). https://doi.org/10.3390/electronics9071150

    Article  Google Scholar 

  71. W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, (2019), doi: https://doi.org/10.1109/TSG.2017.2753802

  72. A. Yu et al., Accurate fault location using deep belief network for optical Fronthaul networks in 5G and beyond. IEEE Access 7, 77932–77943 (2019). https://doi.org/10.1109/ACCESS.2019.2921329

    Article  Google Scholar 

  73. Z.A. Khan, S. Zubair, K. Imran, R. Ahmad, S.A. Butt, N.I. Chaudhary, A new users rating-trend based collaborative Denoising auto-encoder for top-N recommender systems. IEEE Access 7, 141287–141310 (2019). https://doi.org/10.1109/ACCESS.2019.2940603

    Article  Google Scholar 

  74. J. Han, S. Miao, Y. Li, W. Yang, H. Yin, A wind farm equivalent method based on multi-view transfer clustering and stack sparse auto encoder. IEEE Access 8, 92827–92841 (2020). https://doi.org/10.1109/ACCESS.2020.2993808

    Article  Google Scholar 

  75. A. Moradzadeh and K. Pourhossein, Location of disk space variations in transformer winding using convolutional neural networks. In 2019 54th International Universities Power Engineering Conference, UPEC 2019 - Proceedings, (2019), pp. 1–5, doi: https://doi.org/10.1109/UPEC.2019.8893596

  76. R.D. Rathor, A. Bharagava, Day ahead regional electrical load forecasting using ANFIS techniques. J Instit. Engineers (India): Series B 101(5), 475–495 (2020). https://doi.org/10.1007/s40031-020-00477-2

    Article  Google Scholar 

  77. G. Dudek, Short-term load forecasting using random forests. Advances in Intelligent Systems and Computing 323, 821–828 (2015)

    Article  Google Scholar 

  78. N. Son, S. Yang, J. Na, Deep neural network and long short-term memory for electric power load forecasting. Appl Sci (Switzerland) 10(18), 6489 (Sep. 2020). https://doi.org/10.3390/APP10186489

    Article  Google Scholar 

  79. M. Tan, S. Yuan, S. Li, Y. Su, H. Li, F.H. He, Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning. IEEE Trans. Power Syst. 35(4), 2937–2948 (Jul. 2020). https://doi.org/10.1109/TPWRS.2019.2963109

  80. S. Pei, H. Qin, L. Yao, Y. Liu, C. Wang, J. Zhou, Multi-step ahead short-term load forecasting using hybrid feature selection and improved long short-term memory network. Energies 13(6), 4121 (Aug. 2020). https://doi.org/10.3390/en13164121

    Article  Google Scholar 

  81. A. Moradzadeh, A. Mansour-Saatloo, B. Mohammadi-Ivatloo, A. Anvari-Moghaddam, Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Applied Sciences (Switzerland) 10(11), 3829 (2020). https://doi.org/10.3390/app10113829

    Article  Google Scholar 

  82. S. Tzafestas, E. Tzafestas, Computational intelligence techniques for short-term electric load forecasting. Journal of Intelligent and Robotic Systems: Theory and Applications 31(1–3), 7–68 (2001). https://doi.org/10.1023/A:1012402930055

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

    Arash Moradzadeh, Amin Mansour-Saatloo & Behnam Mohammadi-Ivatloo

  2. Department of Architectural Engineering, Pennsylvania State University, State College, PA, USA

    Morteza Nazari-Heris & Somayeh Asadi

Authors
  1. Arash Moradzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amin Mansour-Saatloo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Morteza Nazari-Heris
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Behnam Mohammadi-Ivatloo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Somayeh Asadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behnam Mohammadi-Ivatloo .

Editor information

Editors and Affiliations

  1. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Morteza Nazari-Heris

  2. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Somayeh Asadi

  3. Department of Energy Technology Aalborg University, Aalborg, Denmark

    Behnam Mohammadi-Ivatloo

  4. Deakin University, Geelong, VIC, Australia

    Moloud Abdar

  5. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Houtan Jebelli

  6. Department of Architectural Engineering, Pennsylvania State University, University Park, PA, USA

    Milad Sadat-Mohammadi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Moradzadeh, A., Mansour-Saatloo, A., Nazari-Heris, M., Mohammadi-Ivatloo, B., Asadi, S. (2021). Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Load Forecasting in Power System. In: Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Abdar, M., Jebelli, H., Sadat-Mohammadi, M. (eds) Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-77696-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77696-1_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77695-4

  • Online ISBN: 978-3-030-77696-1

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation