Published March 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Power Demand Forecasting Using ANN and Prophet Models for the Load Despatch Center in Andhra Pradesh, India

Creators

  • 1. Assistant Executive Engineer, Department of Andhra Pradesh State Load Despatch Center, Transmission Corporation of Andhra Pradesh Limited, Vijayawada (Andhra Pradesh), India.

Contributors

Contact person:

  • 1. Assistant Executive Engineer, Department of Andhra Pradesh State Load Despatch Center, Transmission Corporation of Andhra Pradesh Limited, Vijayawada (Andhra Pradesh), India.
  • 2. Statistical Officer, Department of Andhra Pradesh State Load Despatch Center, Transmission Corporation of Andhra Pradesh Limited, Vijayawada (Andhra Pradesh), India.
  • 3. Deputy Executive Engineer, Department of Andhra Pradesh State Load Despatch Center, Transmission Corporation of Andhra Pradesh Limited, Vijayawada (Andhra Pradesh), India.
  • 4. Executive Engineer, Department of Andhra Pradesh State Load Despatch Center, Transmission Corporation of Andhra Pradesh Limited, Vijayawada (Andhra Pradesh), India.

Description

Abstract: This paper uses various data variables to develop and analyze ANN and Prophet models for power demand forecasting in Andhra Pradesh, India. The electricity power consumption in Andhra Pradesh was about 51,756.000 GWh in 2021. Currently, there is a great emphasis on saving power. Power Demand Forecasting is creating much interest, and many models, such as artificial neural networks combined with other techniques based on real-life phenomena, are used and tested. These models have become an essential part of the power and energy sector. This paper considered specific time-series analysis methods and deep-learning techniques for short-term power demand forecasting. This paper also analyzes and compares results between the prophet and ANN models to predict power demand in Andhra Pradesh, India. Our results comparatively revealed the model's appropriateness for the problem. Both models performed well in three performance metrics: accuracy, generalization, and robustness. However, the AI model exhibits better accuracy than Prophet for the historical data set. The time taken for model fitting is also comparatively less for the AI models. The forecast accuracy of the electricity was in the range of 95 to 97.65.

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Additional details

Identifiers

DOI
10.35940/ijsce.A3623.14010324
EISSN
2231-2307

Dates

Accepted
2024-03-15
Manuscript received on 27 February 2024 | Revised Manuscript received on 09 March 2024 | Manuscript Accepted on 15 March 2024 | Manuscript published on 30 March 2024.

References

  • H. Hamedmoghadam, N. Joorabloo, and M. Jalili, "Australia's long-term electricity demand forecasting.pdf".
  • B. J. Chen, M. W. Chang, and C. J. Lin, "Load forecasting using support vector machines: A study on EUNITE Competition 2001," IEEE Transactions on Power Systems, vol. 19, no. 4, pp. 1821–1830, 2004, doi: 10.1109/TPWRS.2004.835679. https://doi.org/10.1109/TPWRS.2004.835679
  • T. Hong, P. Pinson, Y. Wang, R. Weron, D. Yang, and H. Zareipour, "Energy Forecasting: A Review and Outlook," IEEE Open Access Journal of Power and Energy, vol. 7, no. April, pp. 376–388, 2020, doi: 10.1109/oajpe.2020.3029979. https://doi.org/10.1109/OAJPE.2020.3029979
  • A. I. Almazrouee, A. M. Almeshal, and A. S. Almutairi, "applied sciences Long-Term Forecasting of Electrical Loads in Kuwait," Appliend Science, vol. 5627, no. 10, pp. 2–17, 2020. https://doi.org/10.3390/app10165627
  • A. I. Almazrouee, A. M. Almeshal, A. S. Almutairi, M. R. Alenezi, S. N. Alhajeri, and F. M. Alshammari, "Forecasting of electrical generation using prophet and multiple seasonality of holt–winters models: A case study of Kuwait," Applied Sciences (Switzerland), vol. 10, no. 23, pp. 1–19, 2020, doi: 10.3390/app10238412. https://doi.org/10.3390/app10238412
  • J. Bedi and D. Toshniwal, "Deep learning framework to forecast electricity demand," Applied Energy, vol. 238, no. July 2018, pp. 1312–1326, 2019, doi: 10.1016/j.apenergy.2019.01.113. https://doi.org/10.1016/j.apenergy.2019.01.113
  • H. K. Alfares and M. Nazeeruddin, "Electric load forecasting: Literature survey and classification of methods," International Journal of Systems Science, vol. 33, no. 1, pp. 23–34, 2002, doi: 10.1080/00207720110067421. https://doi.org/10.1080/00207720110067421
  • W. He, "Load Forecasting via Deep Neural Networks," Procedia Computer Science, vol. 122, pp. 308–314, 2017, doi: 10.1016/j.procs.2017.11.374. https://doi.org/10.1016/j.procs.2017.11.374
  • L. Ekonomou, "Greek long-term energy consumption prediction using artificial neural networks," Energy, vol. 35, no. 2, pp. 512–517, 2010, doi: 10.1016/j.energy.2009.10.018. https://doi.org/10.1016/j.energy.2009.10.018
  • L. Suganthi and A. A. Samuel, "Energy models for demand forecasting - A review," Renewable and Sustainable Energy Reviews, vol. 16, no. 2, pp. 1223–1240, 2012, doi: 10.1016/j.rser.2011.08.014. https://doi.org/10.1016/j.rser.2011.08.014
  • J. Johannesen, M. Kolhe, and M. Goodwin, "Relative evaluation of regression tools for urban area electrical energy demand forecasting," Journal of Cleaner Production, vol. 218, pp. 555–564, 2019, doi: 10.1016/j.jclepro.2019.01.108. https://doi.org/10.1016/j.jclepro.2019.01.108
  • Y. T. Chen, E. W. Sun, and Y. B. Lin, "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, vol. 56, no. 2, pp. 569–597, 2020, doi: 10.1007/s10614-019-09960-5. https://doi.org/10.1007/s10614-019-09960-5
  • N. Ghadimi, A. Akbarimajd, H. Shayeghi, and O. Abedinia, "Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting," Energy, vol. 161, pp. 130–142, 2018, doi: 10.1016/j.energy.2018.07.088. https://doi.org/10.1016/j.energy.2018.07.088
  • A. Conevska and J. Urpelainen, "Weathering electricity demand? Seasonal variation in electricity consumption among off-grid households in rural India," Energy Research and Social Science, vol. 65, no. June 2019, p. 101444, 2020, doi: 10.1016/j.erss.2020.101444. https://doi.org/10.1016/j.erss.2020.101444
  • H. S. Hippert, C. E. Pedreira, and R. C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 44–55, 2001, doi: 10.1109/59.910780. https://doi.org/10.1109/59.910780
  • T. Hong and S. Fan, "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, vol. 32, no. 3, pp. 914–938, 2016, doi: 10.1016/j.ijforecast.2015.11.011. https://doi.org/10.1016/j.ijforecast.2015.11.011
  • H. X. Zhao and F. Magoulès, "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, vol. 16, no. 6, pp. 3586–3592, 2012, doi: 10.1016/j.rser.2012.02.049. https://doi.org/10.1016/j.rser.2012.02.049
  • R. J. Hyndman and S. Fan, "Density forecasting for long-term peak electricity demand," IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 1142–1153, 2010, doi: 10.1109/TPWRS.2009.2036017. https://doi.org/10.1109/TPWRS.2009.2036017
  • H. Shi, M. Xu, and R. Li, "Deep Learning for Household Load Forecasting-A Novel Pooling Deep RNN," IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 5271–5280, Sep. 2018, doi: 10.1109/TSG.2017.2686012. https://doi.org/10.1109/TSG.2017.2686012
  • C. Feng, M. Sun, and J. Zhang, "Reinforced Deterministic and Probabilistic Load Forecasting via Q-Learning Dynamic Model Selection," IEEE Trans. Smart Grid, vol. 11, no. 2, pp. 1377–1386, 2020. https://doi.org/10.1109/TSG.2019.2937338
  • Shahiduzzaman, K. M., Jamal, M. N., & Nawab, Md. R. I. (2021). Renewable Energy Production Forecasting: A Comparative Machine Learning Analysis. In International Journal of Engineering and Advanced Technology (Vol. 10, Issue 6, pp. 11–18). https://doi.org/10.35940/ijeat.e2689.0810621
  • Vendoti, S., Muralidhar, Dr. M., & Kiranmayi, Dr. R. (2019). Performance Analysis of Hybrid Power System Along With Conventional Energy Sources for Sustainable Development in Rural Areas. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 5971–5977). https://doi.org/10.35940/ijrte.f2567.098319
  • Malyada, C., Keerthana, R., Rao, Dr. P. V. R. D. P., & Keerthana, R. (2020). Prediction of Electricity usage in Industries by Big Data. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 3, pp. 3059–3062). https://doi.org/10.35940/ijitee.c8375.019320
  • Das, S., S, S., M, A., & Jayaram, S. (2021). Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 1, Issue 2, pp. 9–13). https://doi.org/10.54105/ijainn.b1011.041221
  • Sharma, T., & Sharma, R. (2024). Smart Grid Monitoring: Enhancing Reliability and Efficiency in Energy Distribution. In Indian Journal of Data Communication and Networking (Vol. 4, Issue 2, pp. 1–4). https://doi.org/10.54105/ijdcn.d7954.04020224