Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
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Time-Series Estimation based AI Algorithm for Energy Management in a Virtual Power Plant System

  • Yeonwoo LEE (Dept. of Information and Communication Eng., Mokpo National University)
  • Received : 2024.02.03
  • Accepted : 2024.02.26
  • Published : 2024.03.30
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Abstract

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

Keywords

Acknowledgement

This research was supported by Research Funds of Mokpo National University in 2023.

References

  1. Ding, M., Guo, Y. & Zhang, J., et al. (2015). Node Vulnerability Assessment for Complex Power Grids Based on Effect Risk Entropy- weighted Fuzzy Comprehensive Evaluation. Journal of Electrical Engineering Technology, 30(3), 214-223.
  2. Bonventi, W., & Godoy, E. P. (2023). Fuzzy logic for renewable energy recommendation and regional consumption forecast using SARIMA and LSTM. Journal of Renewable and Sustainable Energy, 15(2).
  3. Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
  4. Brockwell, P. J., & Davis, R. A. (2002). Introduction to time series and forecasting. Springer.
  5. Chaturvedi, S., Rajasekar, E., Natarajan, S., & McCullen, N. (2022). A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India. Energy Policy, 168, 113097.
  6. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
  7. Dataset, Korea Power Exchange (KPX), Electrical Power Statistical Information System (EPSIS), http://https://epsis.kpx.or.kr/epsisnew/selectEkmaPtdBftChart.do?menuId=040501
  8. Dubey, A. K., Kumar, A., Garcia-Diaz, V., Sharma, A. K., & Kanhaiya, K. (2021). Study and analysis of SARIMA and LSTM in forecasting time series data. Sustainable Energy Technologies and Assessments, 47, 101474.
  9. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
  10. Hamilton, J. D. (2020). Time series analysis. Princeton University Press.
  11. Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.
  12. He, K., Ji, L., Wu, C. W. D., & Tso, K. F. G. (2021). Using SARIMA-CNN-LSTM approach to forecast daily tourism demand. Journal of Hospitality and Tourism Management, 49, 25-33.
  13. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  14. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal of Basic Engineering, 82(D), 35-45. https://doi.org/10.1115/1.3662552
  15. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.
  16. Lee, Y. (2023). Importance Assessment of Multiple Microgrids Network Based on Modified PageRank Algorithm. Korean Journal of Artificial Intelligence, 11(2), 1-6.
  17. Lee, Y. (2022). Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP. Computers, Materials and Continua, 75(1), 2313-2329. https://doi.org/10.32604/cmc.2023.031133
  18. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X