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Evaluating the EEMD-LSTM model for short-term forecasting of industrial power load: A case study in Vietnam

1School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Viet Nam

2Faculty of Energy Technology Electric Power University, Viet Nam

Received: 3 Jun 2023; Revised: 28 Jul 2023; Accepted: 2 Aug 2023; Available online: 6 Aug 2023; Published: 1 Sep 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
This paper presents the effectiveness of the ensemble empirical mode decomposition-long short-term memory (EEMD-LSTM) model for short term load prediction. The prediction performance of the proposed model is compared to that of three other models (LR, ANN, LSTM). The contribution of this research lay in developing a novel approach that combined the EEMD-LSTM model to enhance the capability of industrial load forecasting. This was a field where there had been limited proposals for improvement, as these hybrid models had primarily been developed for other industries such as solar power, wind power, CO2 emissions, and had not been widely applied in industrial load forecasting before. First, the raw data was preprocessed using the IQR method, serving as the input for all four models. Second, the processed data was then used to train the four models. The performance of each model was evaluated using regression-based metrics such as mean absolute error (MAE) and mean squared error (MSE) to assess their respective output. The effectiveness of the EEMD-LSTM model was evaluated using Seojin industrial load data in Vietnam, and the results showed that it outperformed other models in terms of RMSE, n-RMSE, and MAPE errors for both 1-step and 24-step forecasting. This highlighted the model's capability to capture intricate and nonlinear patterns in electricity load data. The study underscored the significance of selecting a suitable model for electricity load forecasting and concluded that the EEMD-LSTM model was a dependable and precise approach for predicting future electricity assets. The model's robust performance and accurate forecasts showcased its potential in assisting decision-making processes in the energy sector.
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Keywords: Load forecasting; short-term; hybrid model; decomposition; EEMD; LSTM.

Article Metrics:

  1. Aasim, Singh, S. N., & Mohapatra, A. (2021). Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model. Applied Soft Computing, 111, 107730. https://doi.org/10.1016/j.asoc.2021.107730
  2. Adedeji, P. A., Akinlabi, S., Ajayi, O., & Madushele, N. (2019). Non-linear autoregressive neural network (NARNET) with SSA filtering for a university energy consumption forecast. Procedia Manufacturing, 33, 176–183. https://doi.org/10.1016/j.promfg.2019.04.022
  3. Afshar, K., & Bigdeli, N. (2011). Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA). Energy, 36(5), 2620–2627. https://doi.org/10.1016/j.energy.2011.02.003
  4. Ahn, E., & Hur, J. (2023). A short-term forecasting of wind power outputs using the enhanced wavelet transform and arimax techniques. Renewable Energy, 212, 394–402. https://doi.org/10.1016/j.renene.2023.05.048
  5. Ali, M., Prasad, R., Xiang, Y., Khan, M., Ahsan Farooque, A., Zong, T., & Yaseen, Z. M. (2021). Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology. Energy Reports, 7, 6700–6717. https://doi.org/10.1016/j.egyr.2021.09.113
  6. Aseeri, A. O. (2023). Effective RNN-Based Forecasting Methodology Design for Improving Short-Term Power Load Forecasts: Application to Large-Scale Power-Grid Time Series. Journal of Computational Science, 68, 101984. https://doi.org/10.1016/j.jocs.2023.101984
  7. Azizi, N., Yaghoubirad, M., Farajollahi, M., & Ahmadi, A. (2023). Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output. Renewable Energy, 206, 135–147. https://doi.org/10.1016/j.renene.2023.01.102
  8. Bento, P. M. R., Pombo, J. A. N., Calado, M. R. A., & Mariano, S. J. P. S. (2019). Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting. Neurocomputing, 358, 53–71. https://doi.org/10.1016/j.neucom.2019.05.030
  9. Bui, L. D., Nguyen, N. Q., Doan, B. V., & Sanseverino, E. R. (2022). Forecasting energy output of a solar power plant in curtailment condition based on LSTM using P/GHI coefficient and validation in training process, a case study in Vietnam. Electric Power Systems Research, 213, 108706. https://doi.org/10.1016/j.epsr.2022.108706
  10. Cannizzaro, D., Aliberti, A., Bottaccioli, L., Macii, E., Acquaviva, A., & Patti, E. (2021). Solar radiation forecasting based on convolutional neural network and ensemble learning. Expert Systems with Applications, 181, 115167. https://doi.org/10.1016/j.eswa.2021.115167
  11. Carolin Mabel, M., & Fernandez, E. (2008). Analysis of wind power generation and prediction using ANN: A case study. Renewable Energy, 33(5), 986–992. https://doi.org/10.1016/j.renene.2007.06.013
  12. Chattopadhyay, D. (2018). Is Pumped Storage Hydroelectric Power Right for Vietnam? https://openknowledge.worldbank.org/server/api/core/bitstreams/6caea2b8-90cd-5665-84c8-73f831378b49/content
  13. Chen, J.-F., Wang, W.-M., & Huang, C.-M. (1995). Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting. Electric Power Systems Research, 34(3), 187–196. https://doi.org/10.1016/0378-7796(95)00977-1
  14. Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., Bao, Y., & Wang, K. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195, 659–670. https://doi.org/10.1016/j.apenergy.2017.03.034
  15. Chiu, M.-C., Hsu, H.-W., Chen, K.-S., & Wen, C.-Y. (2023). A hybrid CNN-GRU based probabilistic model for load forecasting from individual household to commercial building. Energy Reports, 9, 94–105. https://doi.org/10.1016/j.egyr.2023.05.090
  16. Ding, S., Zhang, Z., Guo, L., & Sun, Y. (2022). An optimized twin support vector regression algorithm enhanced by ensemble empirical mode decomposition and gated recurrent unit. Information Sciences, 598, 101–125. https://doi.org/10.1016/j.ins.2022.03.060
  17. Duc Luong, N. (2015). A critical review on Energy Efficiency and Conservation policies and programs in Vietnam. Renewable and Sustainable Energy Reviews, 52, 623–634. https://doi.org/10.1016/j.rser.2015.07.161
  18. Fan, S., Chen, L., & Lee, W.-J. (2008). Machine learning based switching model for electricity load forecasting. Energy Conversion and Management, 49(6), 1331–1344. https://doi.org/10.1016/j.enconman.2008.01.008
  19. Feng, C., Zhang, J., Zhang, W., & Hodge, B.-M. (2022). Convolutional neural networks for intra-hour solar forecasting based on sky image sequences. Applied Energy, 310, 118438. https://doi.org/10.1016/j.apenergy.2021.118438
  20. Gao, X., Guo, W., Mei, C., Sha, J., Guo, Y., & Sun, H. (2023). Short-term wind power forecasting based on SSA-VMD-LSTM. Energy Reports, 9, 335–344. https://doi.org/10.1016/j.egyr.2023.05.181
  21. Haider, S. A., Sajid, M., Sajid, H., Uddin, E., & Ayaz, Y. (2022). Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad. Renewable Energy, 198, 51–60. https://doi.org/10.1016/j.renene.2022.07.136
  22. Hong, W.-C. (2009). Electric load forecasting by support vector model. Applied Mathematical Modelling, 33(5), 2444–2454. https://doi.org/10.1016/j.apm.2008.07.010
  23. Imani, M. (2021). Electrical load-temperature CNN for residential load forecasting. Energy, 227, 120480. https://doi.org/10.1016/j.energy.2021.120480
  24. Jiang, Z., Che, J., & Wang, L. (2021). Ultra-short-term wind speed forecasting based on EMD-VAR model and spatial correlation. Energy Conversion and Management, 250, 114919. https://doi.org/10.1016/j.enconman.2021.114919
  25. Jurado, M., Samper, M., & Rosés, R. (2023). An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting. Electric Power Systems Research, 217, 109153. https://doi.org/10.1016/j.epsr.2023.109153
  26. Lahouar, A., & Ben Hadj Slama, J. (2015). Day-ahead load forecast using random forest and expert input selection. Energy Conversion and Management, 103, 1040–1051. https://doi.org/10.1016/j.enconman.2015.07.041
  27. Li, D., Sun, G., Miao, S., Gu, Y., Zhang, Y., & He, S. (2022). A short-term electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence. International Journal of Electrical Power & Energy Systems, 137, 107627. https://doi.org/10.1016/j.ijepes.2021.107627
  28. Liu, X., Lin, Z., & Feng, Z. (2021). Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM. Energy, 227, 120492. https://doi.org/10.1016/j.energy.2021.120492
  29. Ma, H., Zhang, C., Peng, T., Nazir, M. S., & Li, Y. (2022). An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting. Energy, 256, 124650. https://doi.org/10.1016/j.energy.2022.124650
  30. Monjoly, S., André, M., Calif, R., & Soubdhan, T. (2017). Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach. Energy, 119, 288–298. https://doi.org/10.1016/j.energy.2016.11.061
  31. Mounir, N., Ouadi, H., & Jrhilifa, I. (2023). Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system. Energy and Buildings, 288, 113022. https://doi.org/10.1016/j.enbuild.2023.113022
  32. Mustaqeem, Ishaq, M., & Kwon, S. (2022). A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting. Sustainable Energy Technologies and Assessments, 52, 102275. https://doi.org/10.1016/j.seta.2022.102275
  33. Muzaffar, S., & Afshari, A. (2019). Short-Term Load Forecasts Using LSTM Networks. Energy Procedia, 158, 2922–2927. https://doi.org/10.1016/j.egypro.2019.01.952
  34. Nguyen, T. H. T., & Phan, Q. B. (2022). Hourly day ahead wind speed forecasting based on a hybrid model of EEMD, CNN-Bi-LSTM embedded with GA optimization. Energy Reports, 8, 53–60. https://doi.org/10.1016/j.egyr.2022.05.110
  35. Nguyen, T. H. T., Phan, Q. B., Nguyen, V. N. N., & Pham, H. M. (2021). Day-ahead electricity load forecasting based on hybrid model of EEMD and Bidirectional LSTM. The 5th International Conference on Future Networks & Distributed Systems, 31–41. https://doi.org/10.1145/3508072.3508079
  36. Pooniwala, N., & Sutar, R. (2021). Forecasting Short-Term Electric Load with a Hybrid of ARIMA Model and LSTM Network. 2021 International Conference on Computer Communication and Informatics (ICCCI), 1–6. https://doi.org/10.1109/ICCCI50826.2021.9402461
  37. Ribeiro, M. H. D. M., Da Silva, R. G., Ribeiro, G. T., Mariani, V. C., & Coelho, L. D. S. (2023). Cooperative ensemble learning model improves electric short-term load forecasting. Chaos, Solitons & Fractals, 166, 112982. https://doi.org/10.1016/j.chaos.2022.112982
  38. Satish, B., Swarup, K. S., Srinivas, S., & Rao, A. H. (2004). Effect of temperature on short term load forecasting using an integrated ANN. Electric Power Systems Research, 72(1), 95–101. https://doi.org/10.1016/j.epsr.2004.03.006
  39. Shi, H., Xu, M., & Li, R. (2018). Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN. IEEE Transactions on Smart Grid, 9(5), 5271–5280. https://doi.org/10.1109/TSG.2017.2686012
  40. Sivakumar, M., S, J. P., George, S. T., Subathra, M. S. P., Leebanon, R., & Kumar, N. M. (2023). Nine novel ensemble models for solar radiation forecasting in Indian cities based on VMD and DWT integration with the machine and deep learning algorithms. Computers and Electrical Engineering, 108, 108691. https://doi.org/10.1016/j.compeleceng.2023.108691
  41. Song, J., Zhang, L., Xue, G., Ma, Y., Gao, S., & Jiang, Q. (2021). Predicting hourly heating load in a district heating system based on a hybrid CNN-LSTM model. Energy and Buildings, 243, 110998. https://doi.org/10.1016/j.enbuild.2021.110998
  42. Trull, O., García-Díaz, J. C., & Peiró-Signes, A. (2022). Multiple seasonal STL decomposition with discrete-interval moving seasonalities. Applied Mathematics and Computation, 433, 127398. https://doi.org/10.1016/j.amc.2022.127398
  43. Vijay, V., Kumar, R., Sharma, A., & Kumar, A. (2022). Short-Term Forecasting of Solar Irradiance using STL, Wavelet and LSTM. International Journal of Computer Applications, 183(46), 9–17. https://doi.org/10.5120/ijca2022921829
  44. Walser, T., & Sauer, A. (2021). Typical load profile-supported convolutional neural network for short-term load forecasting in the industrial sector. Energy and AI, 5, 100104. https://doi.org/10.1016/j.egyai.2021.100104
  45. Wang, D., Yue, C., & ElAmraoui, A. (2021). Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy. Chaos, Solitons & Fractals, 152, 111453. https://doi.org/10.1016/j.chaos.2021.111453
  46. Wang, Y., Sun, S., Chen, X., Zeng, X., Kong, Y., Chen, J., Guo, Y., & Wang, T. (2021). Short-term load forecasting of industrial customers based on SVMD and XGBoost. International Journal of Electrical Power & Energy Systems, 129, 106830. https://doi.org/10.1016/j.ijepes.2021.106830
  47. Wei, N., Yin, L., Li, C., Wang, W., Qiao, W., Li, C., Zeng, F., & Fu, L. (2022). Short-term load forecasting using detrend singular spectrum fluctuation analysis. Energy, 256, 124722. https://doi.org/10.1016/j.energy.2022.124722
  48. World Energy Outlook 2019 – Analysis. (n.d.). IEA. Retrieved July 25, 2023, from https://www.iea.org/reports/world-energy-outlook-2019
  49. Wu, K., Peng, X., Chen, Z., Su, H., Quan, H., & Liu, H. (2023). A novel short-term household load forecasting method combined BiLSTM with trend feature extraction. Energy Reports, 9, 1013–1022. https://doi.org/10.1016/j.egyr.2023.05.041
  50. Zhang, C., Peng, T., & Nazir, M. S. (2022). A novel integrated photovoltaic power forecasting model based on variational mode decomposition and CNN-BiGRU considering meteorological variables. Electric Power Systems Research, 213, 108796. https://doi.org/10.1016/j.epsr.2022.108796
  51. Zhang, L., Alahmad, M., & Wen, J. (2021). Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study. Energy and Buildings, 231, 110592. https://doi.org/10.1016/j.enbuild.2020.110592
  52. Zhang, Q., Wu, J., Ma, Y., Li, G., Ma, J., & Wang, C. (2022). Short-term load forecasting method with variational mode decomposition and stacking model fusion. Sustainable Energy, Grids and Networks, 30, 100622. https://doi.org/10.1016/j.segan.2022.100622
  53. Zhu, Z., Zhou, M., Hu, F., Wang, S., Ma, J., Gao, B., Bian, K., & Lai, W. (2023). A day-ahead industrial load forecasting model using load change rate features and combining FA-ELM and the AdaBoost algorithm. Energy Reports, 9, 971–981. https://doi.org/10.1016/j.egyr.2022.12.044

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