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An ensemble approach for electricity price forecasting in markets with renewable energy resources

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  • Bhatia, Kushagra
  • Mittal, Rajat
  • Varanasi, Jyothi
  • Tripathi, M.M.

Abstract

With the restructuring of formerly vertically integrated utilities, the energy market behaves like a competitive market, which has resulted in an increased focus on the formulation of forecasting techniques. The contribution of this work is twofold. Firstly, we analyze and evaluate the impact of renewable sources on price forecasts and use them in model training. Next, we propose a bootstrap aggregated-stack generalized architecture for very short-term electricity price forecasting to facilitate market participants in formulating strategies in real time. The stacking phase integrates extreme gradient boosting and random forest, which is then bagged to obtain a computationally efficient model. The final combination of feature engineering and ensemble architecture is observed to outperform the existing techniques.

Suggested Citation

  • Bhatia, Kushagra & Mittal, Rajat & Varanasi, Jyothi & Tripathi, M.M., 2021. "An ensemble approach for electricity price forecasting in markets with renewable energy resources," Utilities Policy, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:juipol:v:70:y:2021:i:c:s0957178721000199
    DOI: 10.1016/j.jup.2021.101185
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    7. Hoxha, Julian & Çodur, Muhammed Yasin & Mustafaraj, Enea & Kanj, Hassan & El Masri, Ali, 2023. "Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis," Applied Energy, Elsevier, vol. 350(C).
    8. Yang, Wendong & Sun, Shaolong & Hao, Yan & Wang, Shouyang, 2022. "A novel machine learning-based electricity price forecasting model based on optimal model selection strategy," Energy, Elsevier, vol. 238(PC).
    9. Kaiyan Wang & Xueyan Wang & Rong Jia & Jian Dang & Yan Liang & Haodong Du, 2022. "Research on Coupled Cooperative Operation of Medium- and Long-Term and Spot Electricity Transaction for Multi-Energy System: A Case Study in China," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
    10. Shayan, Mostafa Esmaeili & Najafi, Gholamhassan & Ghobadian, Barat & Gorjian, Shiva & Mamat, Rizalman & Ghazali, Mohd Fairusham, 2022. "Multi-microgrid optimization and energy management under boost voltage converter with Markov prediction chain and dynamic decision algorithm," Renewable Energy, Elsevier, vol. 201(P2), pages 179-189.
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    12. Singh, Priyanka & Kottath, Rahul, 2022. "Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices," Utilities Policy, Elsevier, vol. 79(C).

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