Skip to main content

Advertisement

SpringerLink
Log in

A hybrid robust system considering outliers for electric load series forecasting

  • Original Submission
  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Electric load forecasting has become crucial to the safe operation of power grids and cost reduction in the production of power. Although numerous electric load forecasting models have been proposed, most of them are still limited by poor effectiveness in the model training and a sensitivity to outliers. The limitations of current methods may lead to extra operational costs of a power system or even disrupt its power distribution and network safety. To this end, we propose a new hybrid load-forecasting model, which is based on a robust extreme-learning machine and an improved whale optimization algorithm. Specifically, Huber loss, which is insensitive to outliers, is proposed as the objective function in extreme learning machine (ELM) training. In addition, an improved whale optimization algorithm is designed for the robust ELM training, in which a cellular automaton mechanism is used to enhance the local search. To verify our improved whale optimization algorithm, some experiments were then conducted based on seven benchmark test functions. Due to the enhancement of the local search, the improved optimizer was around 7% superior to the basic. Finally, our proposed hybrid forecasting model was validated by two real electric load datasets (Nanjing and New South Wales), and the experimental results confirmed that the proposed hybrid load-forecasting model could achieve satisfying improvements in both datasets.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Kong W, Dong Z Y, Jia Y, Hill D J, Xu Y, Zhang Y (2017) Short-term residential load forecasting based on lstm recurrent neural network. IEEE Trans Smart Grid 10(1):841–851

    Article  Google Scholar 

  2. Guo W, Che L, Shahidehpour M, Wan X (2021) Machine-learning based methods in short-term load forecasting. Electr J 34(1):106884

    Article  Google Scholar 

  3. Zareipour H, Canizares C A, Bhattacharya K (2009) Economic impact of electricity market price forecasting errors: a demand-side analysis. IEEE Trans Power Syst 25(1):254–262

    Article  Google Scholar 

  4. Xie J, Chen Y, Hong T, Laing T D (2016) Relative humidity for load forecasting models. IEEE Trans Smart Grid 9(1):191–198

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Chen K, Chen K, Wang Q, He Z, Hu J, He J (2018) Short-term load forecasting with deep residual networks. IEEE Trans Smart Grid 10(4):3943–3952

    Article  Google Scholar 

  7. Cui Z, Wu J, Ding Z, Duan Q, Lian W, Yang Y, Cao T (2021) A hybrid rolling grey framework for short time series modelling. Neural Comput Appl:1–15

  8. Heydari A, Nezhad M M, Pirshayan E, Garcia D A, Keynia F, De Santoli L (2020) 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

    Article  Google Scholar 

  9. Xu W, Peng H, Zeng X, Zhou F, Tian X, Peng X (2019) A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl Intell 49(8):3002– 3015

    Article  Google Scholar 

  10. Lindberg KB, Seljom P, Madsen H, Fischer D, Korpås M (2019) Long-term electricity load forecasting: Current and future trends. Util Policy 58:102–119

    Article  Google Scholar 

  11. Dong M, Grumbach L (2019) A hybrid distribution feeder long-term load forecasting method based on sequence prediction. IEEE Trans Smart Grid 11(1):470–482

    Article  Google Scholar 

  12. Al Mamun A, Sohel M, Mohammad N, Sunny M S H, Dipta D R, Hossain E (2020) A comprehensive review of the load forecasting techniques using single and hybrid predictive models. IEEE Access 8:134911–134939

    Article  Google Scholar 

  13. Zhou M, Jin M (2017) Holographic ensemble forecasting method for short-term power load. IEEE Trans Smart Grid 10(1):425–434

    Article  Google Scholar 

  14. 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 

  15. Zhang S, Wang Y, Zhang Y, Wang D, Zhang N (2020) Load probability density forecasting by transforming and combining quantile forecasts. Appl Energy 277:115600

    Article  Google Scholar 

  16. Ye F, Zhang L, Zhang D, Fujita H, Gong Z (2016) A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inf Sci 367:41–57

    Article  MATH  Google Scholar 

  17. López J C, Rider M J, Wu Q (2018) Parsimonious short-term load forecasting for optimal operation planning of electrical distribution systems. IEEE Trans Power Syst 34(2):1427– 1437

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Wu Z, Zhao X, Ma Y, Zhao X (2019) A hybrid model based on modified multi-objective cuckoo search algorithm for short-term load forecasting. Appl Energy 237:896–909

    Article  Google Scholar 

  20. Ye M, Wang H (2020) Robust adaptive integral terminal sliding mode control for steer-by-wire systems based on extreme learning machine. Comput Electr Eng 86:106756

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Alipour M, Aghaei J, Norouzi M, Niknam T, Hashemi S, Lehtonen M (2020) A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration. Energy 205:118106

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Sheng Z, Wang H, Chen G, Zhou B, Sun J (2020) Convolutional residual network to short-term load forecasting. Appl Intell:1–15

  25. Elattar E E, Sabiha N A, Alsharef M, Metwaly M K, Abd-Elhady A M, Taha Ibrahim BM (2020) Short term electric load forecasting using hybrid algorithm for smart cities. Appl Intell 50:3379–3399

    Article  Google Scholar 

  26. Bedi J, Toshniwal D (2020) Energy load time-series forecast using decomposition and autoencoder integrated memory network. Appl Soft Comput 93:106390

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Wu J, Wang Y-G, Tian Y-C, Burrage K, Cao T (2021) Support vector regression with asymmetric loss for optimal electric load forecasting. Energy 223:119969

    Article  Google Scholar 

  29. Fan G-F, Peng L-L, Hong W-C, Sun F (2016) Electric load forecasting by the svr model with differential empirical mode decomposition and auto regression. Neurocomputing 173:958–970

    Article  Google Scholar 

  30. Li G, Li Y, Roozitalab F (2020) Midterm load forecasting: A multistep approach based on phase space reconstruction and support vector machine. IEEE Syst J 14(4):4967–4977

    Article  Google Scholar 

  31. Wu J, Cui Z, Chen Y, Kong D, Wang Y-G (2019) A new hybrid model to predict the electrical load in five states of australia. Energy 166:598–609

    Article  Google Scholar 

  32. Jnr E O-N, Ziggah Y Y, Relvas S (2021) Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting. Sustainable Cit Soc 66:102679

    Article  Google Scholar 

  33. Li J, Deng D, Zhao J, Cai D, Hu W, Zhang M, Huang Q (2020) A novel hybrid short-term load forecasting method of smart grid using mlr and lstm neural network. IEEE Transactions on Industrial Informatics

  34. Liang Y, Niu D, Hong W-C (2019) Short term load forecasting based on feature extraction and improved general regression neural network model. Energy 166:653–663

    Article  Google Scholar 

  35. Tang X, Dai Y, Wang T, Chen Y (2019) Short-term power load forecasting based on multi-layer bidirectional recurrent neural network. IET Gener Transmiss Distrib 13(17):3847–3854

    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. Dong Y, Ma X, Fu T (2021) Electrical load forecasting: A deep learning approach based on k-nearest neighbors. Appl Soft Comput 99:106900

    Article  Google Scholar 

  38. Fekri M N, Patel H, Grolinger K, Sharma V (2021) Deep learning for load forecasting with smart meter data: Online adaptive recurrent neural network. Appl Energy 282:116177

    Article  Google Scholar 

  39. Yin L, Xie J (2021) Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems. Appl Energy 283:116328

    Article  Google Scholar 

  40. Yang J, Cao J, Wang T, Xue A, Chen B (2020) Regularized correntropy criterion based semi-supervised elm. Neural Netw 122:117–129

    Article  MATH  Google Scholar 

  41. Su B, Mu R, Long T, Li Y, Cui N (2020) Variational bayesian adaptive high-degree cubature huber-based filter for vision-aided inertial navigation on asteroid missions. IET Radar Sonar Navigation 14(9):1391–1401

    Article  Google Scholar 

  42. Mirjalili S, Gandomi A H, Mirjalili S Z, Saremi S, Faris H, Mirjalili S M (2017) Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. Bo H, Nie Y, Wang J (2020) Electric load forecasting use a novelty hybrid model on the basic of data preprocessing technique and multi-objective optimization algorithm. IEEE Access 8:13858–13874

    Article  Google Scholar 

  45. Mirjalili S, Mirjalili S M, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  46. Xu J, Tan W, Li T (2020) Predicting fan blade icing by using particle swarm optimization and support vector machine algorithm. Comput Electr Eng 87:106751

    Article  Google Scholar 

  47. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  MathSciNet  Google Scholar 

  48. Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  49. Mirjalili S (2015) Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  50. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  51. Gao Y, Qian C, Tao Z, Zhou H, Wu J, Yang Y (2020) Improved whale optimization algorithm via cellular automata. In: 2020 IEEE International Conference on Progress in Informatics and Computing (PIC). IEEE, pp 34–39

  52. Lu C, Gao L, Yi J (2018) Grey wolf optimizer with cellular topological structure. Expert Syst Appl 107:89–114

    Article  Google Scholar 

  53. Ma J, Liu H, Peng C, Qiu T (2020) Unauthorized broadcasting identification: A deep lstm recurrent learning approach. IEEE Trans Instrum Meas 69(9):5981–5983

    Article  Google Scholar 

  54. Zhang B, Tan R, Lin C-J (2020) Forecasting of e-commerce transaction volume using a hybrid of extreme learning machine and improved moth-flame optimization algorithm. Appl Intell:1–14

  55. Ge J, Li H, Wang H, Dong H, Liu H, Wang W, Yuan Z, Zhu J, Zhang H (2019) Aeromagnetic compensation algorithm robust to outliers of magnetic sensor based on huber loss method. IEEE Sens J 19(14):5499–5505

    Article  Google Scholar 

  56. Chen G, Li L, Zhang Z, Li S (2020) Short-term wind speed forecasting with principle-subordinate predictor based on conv-lstm and improved bpnn. IEEE Access 8:67955–67973

    Article  Google Scholar 

  57. Zhang J, Teng Y-F, Chen W (2019) Support vector regression with modified firefly algorithm for stock price forecasting. Appl Intell 49(5):1658–1674

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grants 61873130, 61833011 and 61833008, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191377 and BK20191376, in part by 1311 Talent Project of Nanjing University of Posts and Telecommunications, in part by Scientific Foundation of Nanjing University of Posts and Telecommunications (NUPTSF) under Grants NY220102, NY220194 and 2020XZZ11. Also, this work was supported by the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), grant number CE140100049.

Author information

Authors and Affiliations

  1. College of Automation, College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China

    Yang Yang, Zhenghang Tao, Chen Qian, Yuchao Gao & Hu Zhou

  2. School of Computer Science, Queensland University of Technology, Queensland, Australia

    Zhe Ding

  3. School of Mathematical Science, Queensland University of Technology, Queensland, Australia

    Jinran Wu

Authors
  1. Yang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenghang Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuchao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhe Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jinran Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinran Wu.

Additional information

Credit authorship contribution statement

Yang Yang: Project administration, Supervision, Investigation Zhenghang Tao: Software, Visualization, Writing - original draft. Yuchao Gao: Visualization, Writing - original draft. Chen Qian: Formal analysis, Writing - original draft Hu Zhou: Writing – review & editing. Zhe Ding: Writing- review & editing. Jinran Wu: Supervision, Project administration,Investigation, Writing – review & editing.

Declaration of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Tao, Z., Qian, C. et al. A hybrid robust system considering outliers for electric load series forecasting. Appl Intell 52, 1630–1652 (2022). https://doi.org/10.1007/s10489-021-02473-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02473-5

Keywords

Search

Navigation