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Intraday time series load forecasting using Bayesian deep learning method—a new approach

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Abstract

The accurate short-term load forecasting can pave the way for improving, planning and maintenance of electric power systems. In this work, a novel Bayesian deep learning framework has been developed on BiLSTM + dropout network for analyzing short-term load prediction. The proposed framework improves the prediction accuracy by learning the uncertainties as well as reducing the data overfitting. Furthermore, a hierarchical clustering technique is applied to group consumers of similar behaviors in utilizing the power to improve the accuracy of load forecasting. The proposed framework is implemented using the MATLAB platform and tested on CER smart meter data. The RMSE, MAE and MAPE benchmark parameters are considered for comparing the proposed framework with the existing state-of-the-art techniques, viz., LSTM dropout, GRU, SVR and ARIMA. From the experimental results, it is evident that the proposed framework has achieved a better prediction accuracy despite the dataset having different load profiles, huge number of customers, uncertainties and volatility.

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References

  1. Deng R, Yang Z, Chow MY, Chen J (2015) A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans Ind Inform 11(3):570–582

    Article  Google Scholar 

  2. Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11(2):2664–2675

    Article  Google Scholar 

  3. Yun Z, Quan Z, Caixin S, Shaolan L, Yuming L, Yang S (2008) RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Trans Power Syst 23(3):853–858

    Article  Google Scholar 

  4. Taylor JW (2012) Short-term load forecasting with exponentially weighted methods. IEEE Trans Power Syst 27(1):458–464

    Article  Google Scholar 

  5. Dejan I, Per GDS, Stamatis K, Malte J (2013) Impact assessment of smart meter grouping on the accuracy of forecasting algorithms. In: SAC '13: proceedings of the 28th annual ACM symposium on applied computing, pp 673–679

  6. Erick MO, Fernando LO (2018) Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy 144:776–788

    Article  Google Scholar 

  7. 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, Florence, pp 7046–7051

  8. Petra V, Anna BE, Viera R, Slavomír S, Arun KS (2018) Smart grid load forecasting using online support vector regression. Comput Electr Eng 65:102–117

    Article  Google Scholar 

  9. Zheng J, Xu C, Zhang Z, Li X (2017) Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In: 51st annual conference on information sciences and systems (CISS), Baltimore, MD, pp 1–6

  10. Alberg D, Last M (2018) Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. Vietnam J Comput Sci 2:241–249

    Article  Google Scholar 

  11. Shahzadeh A, Khosravi A, Nahavandi S (2015) Improving load forecast accuracy by clustering consumers using smart meter data. In: International joint conference on neural networks (IJCNN), Killarney, pp 1–7

  12. Siami-Namini S, Tavakoli N, Namin AS (2019) A comparative analysis of forecasting financial time series using ARIMA, LSTM, and BiLSTM. abs/1911.09512

  13. Zhang R, Dong Y, Xu Y, Meng K, Wong KP (2013) Short-term load forecasting of Australian National Electricity Market by an ensemble model of an extreme learning machine. IET Gener Transm Distrib 7(4):391–397

    Article  Google Scholar 

  14. Chaouch M (2014) Clustering-based improvement of nonparametric functional time series forecasting: application to intra-day household-level load curves. IEEE Trans Smart Grid 5(1):411–419

    Article  Google Scholar 

  15. Ryu S, Noh J, Kim H. Deep neural network based demand side short term load forecasting. In: Proceedings of 2016 IEEE international conference on smart grid communications (SmartGridComm), Sydney, NSW, 2016, pp 308–313

  16. Fan G-F, Guo Y-H, Zheng J-M, Hong W-C (2019) Application of the weighted K-nearest neighbor algorithm for short-term load forecasting. Energies 12:916

    Article  Google Scholar 

  17. Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang H (2017) Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid 10(1):841–851

    Article  Google Scholar 

  18. Kwac J, Flora J, Rajagopal R (2014) Household energy consumption segmentation using hourly data. IEEE Trans Smart Grid 5(1):420–430

    Article  Google Scholar 

  19. Quilumba FL, Lee W, Huang H, Wang DY, Szabados RL (2015) Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities. IEEE Trans Smart Grid 6(2):911–918

    Article  Google Scholar 

  20. Stephen B, Tang X, Harvey PR, Galloway S, Jennett KI (2017) Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting. IEEE Trans Smart Grid 8(4):1591–1598

    Article  Google Scholar 

  21. Wang Y, Chen Q, Sun M, Kang C, Xia Q (2018) An ensemble forecasting method for the aggregated load with subprofiles. IEEE Trans Smart Grid 9(4):3906–3908

    Article  Google Scholar 

  22. Chen H, Wang S, Wan S, Li Y (2019) Day-ahead aggregated load forecasting based on two-terminal sparse coding and deep neural network fusion. Electric Power Syst Res 177:105987

    Article  Google Scholar 

  23. Fahiman F, Erfani SM, Rajasegarar S, Palaniswami M, Leckie C (2017) Improving load forecasting based on deep learning and K-shape clustering. In: 2017 international joint conference on neural networks (IJCNN), Anchorage, AK, pp 4134–4141 (2017)

  24. Gajowniczek K, Ząbkowski T (2018) Simulation study on clustering approaches for short-term electricity forecasting. Complexity

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

    Article  Google Scholar 

  26. Yang Y, Li W, Gulliver TA, Li S (2020) Bayesian deep learning-based probabilistic load forecasting in smart grids. IEEE Trans Ind Inform 16(7):4703–4713

    Article  Google Scholar 

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Acknowledgements

This research work is supported by CIT—TLC under the PMMMNMTT scheme of MHRD, Govt. of India. The authors sincerely acknowledge the Commission for Energy Regulation (CER) and Irish Social Science Data Archive (ISSDA) for providing smart meter dataset.

Funding

Funding was provided by Ministry of Human Resource Development (Grant No. 1-25/2017-PN.II).

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Authors and Affiliations

  1. Teaching Learning Centre, Coimbatore Institute of Technology, Coimbatore, India

    D. Kiruthiga

  2. Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India

    V. Manikandan

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  1. D. Kiruthiga
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  2. V. Manikandan
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Correspondence to D. Kiruthiga.

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Kiruthiga, D., Manikandan, V. Intraday time series load forecasting using Bayesian deep learning method—a new approach. Electr Eng 104, 1697–1709 (2022). https://doi.org/10.1007/s00202-021-01411-5

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