• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 11, Pages: 1016-1027

Original Article

A Multi-step Short-term Load Forecasting using Hybrid DNN and GAF

Received Date:27 December 2023, Accepted Date:08 February 2024, Published Date:29 February 2024


Background: Short-term Load Forecasting (STLF) is vital for grid stability, ensuring a steady power supply and resource efficiency. However, the literature review underscores imperfections in current methods, emphasizing the necessity for additional research in this domain. Objectives: This study introduces an effective framework for multi-step STLF, enhancing predictive accuracy by integrating state-of-the-art DNN models like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) in a hybrid architecture, leveraging their complementary strengths. Method : A parallel hybrid network consisting of LSTM and a Two-Dimensional Convolutional Neural Network (2D-CNN), which operates load sequence input and 2D converted load input, is employed. Their combined context vector is finally used for predicting the preceding 24 load sequences. Extensive comparative testing involves various existing STLF methods (Artificial Neural Network (ANN), LSTM Sequence-to-Sequence (LSTM-S2S), Gated Recurrent Unit with Genetic Algorithm (GRU-GA), CNN-LSTM, GRU-CNN, and Parallel LSTM and CNN Network (PLCNet)) experimented across nine load datasets, using Mean Absolute Percentage Error (MAPE) as the evaluation metric. Findings: The proposed model demonstrates enhanced MAPE, with values of 3.34, 5.67, 4.92, 4.84, 5.24, 4.73, 5.15, 5.49, and 3.96 across nine datasets. The critical distance diagram further validates these results. The findings from this comparative analysis underscore the efficacy of the proposed multi-step STLF method, showcasing significant improvements in forecasting accuracy. Novelty: The presented network architecture is novel in its fusion of LSTM and 2D-CNN networks through the Gramian Angular Field (GAF) technique. Additionally, the parallel operation of LSTM and 2D-CNN generates distinct context vectors combined to form the final output.

Keywords: Parallel CNN­LSTM, Gramian Angular Field, Electricity Demand Prediction, Short-term Load Forecasting, Multi-step Forecasting


  1. Eren Y, Küçükdemiral İ. A comprehensive review on deep learning approaches for short-term load forecasting. Renewable and Sustainable Energy Reviews. 2024;189:114031. Available from: https://doi.org/10.1016/j.rser.2023.114031
  2. Ullah I, Hasanat SM, Aurangzeb K, Alhussein M, Rizwan M, Anwar MS. Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution. PeerJ Computer Science. 2023;9:e1487. Available from: https://doi.org/10.7717/peerj-cs.1487
  3. Chapagain K, Gurung S, Kulthanavit P, Kittipiyakul S. Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data. Applied System Innovation. 2023;6(6):100. Available from: https://doi.org/10.3390/asi6060100
  4. Kiranyaz S, Onuravci O, Abdeljaber T, Ince, Moncefgabbouj DJ, Inman. 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing . 2021;151:107398. Available from: https://doi.org/10.1016/j.ymssp.2020.107398
  5. Pirbazari AM, Sharma E, Chakravorty A, Elmenreich W, Rong C. An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities. IEEE Access. 2021;9:36218–36240. Available from: https://doi.org/10.1109/ACCESS.2021.3063066
  6. Abumohsen M, Owda AY, Owda MY. Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms. Energies. 2023;16(5):2283. Available from: https://doi.org/10.3390/en16052283
  7. Agga FA, Abbou SA, Houm YE, Labbadi M. Short-Term Load Forecasting Based on CNN and LSTM Deep Neural Networks. IFAC-PapersOnLine. 2022;55(12):777–781. Available from: https://hal.science/hal-03809816/
  8. Sajjad M, Khan ZA, Ullah A, Hussain T, Ullah W, Lee MY, et al. A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting. IEEE Access. 2020;8:143759–143768. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9141253
  9. Li C, Hu R, Hsu CY, Han Y, Hua H, Liu M, et al. An ensemble framework for short-term load forecasting based on parallel CNN and GRU with improved ResNet. Electric Power Systems Research. 2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS). 2023;10:109057. Available from: https://ieeexplore.ieee.org/document/9873566
  10. Hua H, Liu M, Li Y, Deng S, Wang Q. An ensemble framework for short-term load forecasting based on parallel CNN and GRU with improved ResNet. Electric Power Systems Research. 2023;216:109057. Available from: https://doi.org/10.1016/j.epsr.2022.109057
  11. Shilpa GN, Sheshadri GS. ANN Based Short Term Load Forecasting for Karnataka State Demand. 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon). ;p. 1–5. Available from: https://www.ijerd.com/paper/vol13-issue7/J1377579.pdf
  12. Pirbazari AM, Sharma E, Chakravorty A, Elmenreich W, Rong C. An Ensemble Approach for Multi-Step Ahead Energy Forecasting of Household Communities. IEEE Access. 2021;9:36218–36240. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9366871
  13. Inteha A, Nahid-Al-Masood. A GRU-GA Hybrid Model Based Technique for Short Term Electrical Load Forecasting. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). 2021;p. 515–519. Available from: https://doi.org/10.109/ICREST51555.2021.9331156
  14. Rafi SH, Nahid-Al-Masood, Deeba SR, Hossain E. A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network. IEEE Access. 2021;9:32436–32448. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9358156
  15. Wu L, Kong C, Hao X, Chen W. A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model. Mathematical Problems in Engineering. 2020;2020:1–10. Available from: https://doi.org/10.1155/2020/1428104
  16. Bhatt D, Patel C, Talsania H, Patel J, Vaghela R, Pandya S, et al. CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope. Electronics. 1920;10(20):2470. Available from: https://doi.org/10.3390/electronics10202470


© 2024 Kshetrimayum et al.  This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.