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Comparison of multi-step forecasting methods for renewable energy

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Abstract

Multi-step forecasting influences systems of energy management a lot, but traditional methods are unable to obtain important feature information because of the complex composition of features, which causes prediction errors. There are numerous types of data to forecast in the energy sector; we present the following datasets for comparison in the paper: electricity demand, PV production, and heating, ventilation, and air conditioning load. For a detailed comparison, we took both classical and modern forecasting methods: Bayesian ridge, Ridge regression, Linear regression, ARD regression, LightGBM, RF, Bi-RNN, Bi-LSTM, Bi-GRU, and XGBoost.

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References

  1. Lee, D., Cheng, C.C.: Energy savings by energy management systems: a review. Renew. Sustain. Energy Rev. 56, 760–777 (2016). https://doi.org/10.1016/j.rser.2015.11.067

    Article  Google Scholar 

  2. Hayes, B.P., Prodanovic, M.: State forecasting and operational planning for distribution network energy management systems. IEEE Trans. Smart Grid 7(2), 1002–1011 (2015)

    Article  Google Scholar 

  3. Chan, S.-C., Tsui, K.M., Wu, H.C., Hou, Y., Wu, Y.-C., Wu, F.F.: Load/price forecasting and managing demand response for smart grids: methodologies and challenges. IEEE Signal Process. Mag. 29(5), 68–85 (2012)

    Article  ADS  Google Scholar 

  4. Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R., Young, W.A.: An overview of energy demand forecasting methods published in 2005–2015. Energy Syst. 8, 411–447 (2017)

    Article  Google Scholar 

  5. Ahmed, R., Sreeram, V., Mishra, Y., Arif, M.D.: A review and evaluation of the state-of-the-art in pv solar power forecasting: techniques and optimization. Renew. Sustain. Energy Rev. 124, 109792 (2020)

    Article  Google Scholar 

  6. Singh, A.S., Khatoon, I.S., Muazzam, M., Chaturvedi, D.K.: An overview of electricity demand forecasting techniques. Netw. Complex Syst. 3(3), 38–48 (2013)

    Google Scholar 

  7. García-Martos, C., Rodríguez, J., Sánchez, M.J.: Modelling and forecasting fossil fuels, co2 and electricity prices and their volatilities. Appl. Energy 101, 363–375 (2013)

    Article  ADS  Google Scholar 

  8. Hodge, B.-M., Martinez-Anido, C.B., Wang, Q., Chartan, E., Florita, A., Kiviluoma, J.: The combined value of wind and solar power forecasting improvements and electricity storage. Appl. Energy 214, 1–15 (2018). https://doi.org/10.1016/j.apenergy.2017.12.120

    Article  ADS  Google Scholar 

  9. Schneider Electric: Official website, (2022). https://www.se.com/ww/en/. Accessed 1 Mar 2022

  10. Solargis: Official website, (2022). https://solargis.com/products/api. Accessed 1 Mar 2022

  11. Alpha Building Synthetic Datase Githab repository: Official website, (2022). https://github.com/LBNL-ETA/AlphaBuilding-SyntheticDatase. Accessed 1 Mar 2022

  12. Ma, R., Zhang, Y., Liu, J., Petrosian, O., Krinkin, K.: Prediction of next app in os. In: 2022 III International conference on neural networks and neurotechnologies (NeuroNT), pp. 28–31. IEEE (2022)

  13. Zhang, Y., Xu, F., Zou, J., Petrosian, O.L., Krinkin, K.V.: Xai evaluation: evaluating black-box model explanations for prediction. In: 2021 II International conference on neural networks and neurotechnologies (NeuroNT), pp. 13–16. IEEE (2021)

  14. Zakharov, V., Balykina, Y., Petrosian, O., Gao, H.: Cbrr model for predicting the dynamics of the covid-19 epidemic in real time. Mathematics 8(10), 1727 (2020)

    Article  Google Scholar 

  15. Zhang, Y., Petrosian, O., Liu, J., Ma, R., Krinkin, K.: Fi-shap: explanation of time series forecasting and improvement of feature engineering based on boosting algorithm. In: Proceedings of SAI intelligent systems conference, pp. 745–758. Springer (2022)

  16. Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., Zareipour, H.: Energy forecasting: a review and outlook. IEEE Open Access J. Power and Energy 7, 376–388 (2020). https://doi.org/10.1109/OAJPE.2020.3029979

    Article  Google Scholar 

  17. Wang, H., Lei, Z., Zhang, X., Zhou, B., Peng, J.: A review of deep learning for renewable energy forecasting. Energy Convers. Manage. 198, 111799 (2019). https://doi.org/10.1016/j.enconman.2019.111799

    Article  Google Scholar 

  18. Deb, C., Zhang, F., Yang, J., Lee, S.E., Shah, K.W.: A review on time series forecasting techniques for building energy consumption. Renew. Sustain. Energy Rev. 74, 902–924 (2017). https://doi.org/10.1016/j.rser.2017.02.085

    Article  Google Scholar 

  19. Zhang, Y., Ma, R., Liu, J., Liu, X., Petrosian, O., Krinkin, K.: Comparison and explanation of forecasting algorithms for energy time series. Mathematics 9(21), 2794 (2021). https://doi.org/10.3390/math9212794

    Article  Google Scholar 

  20. Sorjamaa, A., Hao, J., Reyhani, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomputing 70(16–18), 2861–2869 (2007)

    Article  Google Scholar 

  21. Hamzaçebi, C., Akay, D., Kutay, F.: Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting. Expert Syst. Appl. 36(2), 3839–3844 (2009)

    Article  Google Scholar 

  22. Bontempi, G.: Long term time series prediction with multi-input multi-output local learning. In: Proceedings of the 2nd European symposium on time series prediction (TSP), pp. 145–154 (2008)

  23. Efendi, A., Effrihan, E.: A simulation study on bayesian ridge regression models for several collinearity levels. In: AIP conference proceedings, vol. 1913. AIP Publishing (2017)

  24. Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)

    Article  Google Scholar 

  25. Nathans, L.L., Oswald, F.L., Nimon, K.: Interpreting multiple linear regression: a guidebook of variable importance. Pract. Assess. Res. Eval. 17(9), n9 (2012). https://doi.org/10.7275/5fex-b874

    Article  Google Scholar 

  26. Paper, D., Paper, D.: Scikit-learn regression tuning. Hands-on Scikit-learn for machine learning applications: data science fundamentals with Python, pp. 189–213 (2020). https://doi.org/10.1007/978-1-4842-5373-1_7

  27. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017)

  28. Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785

  29. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)

    Article  Google Scholar 

  30. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  CAS  PubMed  Google Scholar 

  31. Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  ADS  Google Scholar 

  32. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv (2014). arXiv:1406.1078

  33. Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m5 accuracy competition: results, findings and conclusions. 2020. https://www.researchgate.net/publication/344487258_The_M5_Accuracy_competition_Results_findings_and_conclusions, (2022)

  34. Taieb, S., Bontempi, G., Atiya, A.F., Sorjamaa, A.: A review and comparison of strategies for multi-step ahead time series forecasting based on the nn5 forecasting competition. Expert Syst. Appl. 39(8), 7067–7083 (2012)

    Article  Google Scholar 

  35. Bontempi, G., Taieb, S.B.: Conditionally dependent strategies for multiple-step-ahead prediction in local learning. Int. J. Forecast. 27(3), 689–699 (2011)

    Article  Google Scholar 

  36. Kline, D. M.: Methods for multi-step time series forecasting neural networks. pp. 226–250, (2004)

  37. Yang, B.-S., Tan, A.C.C., et al.: Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems. Expert Syst. Appl. 36(5), 9378–9387 (2009)

    Article  Google Scholar 

  38. Saad, E.W., Prokhorov, D.V., Wunsch, D.C.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Trans. Neural Netw. 9(6), 1456–1470 (1998)

    Article  CAS  PubMed  Google Scholar 

  39. Bontempi, G., Birattari, M., Bersini, H.: Local learning for iterated time-series prediction. In: Machine Learning: Proceedings of the Sixteenth International Conference, pp. 32–38 (1999)

  40. Scikit-learn MultiOutputRegressor: Official website (2022). https://scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputRegressor.html. Accessed 1 Mar 2022

  41. Yang, L., Shami, A.: On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing 415, 295–316 (2020)

    Article  Google Scholar 

  42. Liashchynskyi, P., Liashchynskyi, P.: Grid search, random search, genetic algorithm: a big comparison for nas. ArXiv, (2019). ARxIV:1912.06059

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Authors and Affiliations

  1. Faculty of Applied Mathematics and Control Processes, Saint-Petersburg State University, Universitetskii Prospekt 35, St. Petersburg, 198504, Russia

    E. Dolgintseva, H. Wu, O. Petrosian, A. Zhadan, A. Allakhverdyan & A. Martemyanov

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  1. E. Dolgintseva
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  2. H. Wu
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  3. O. Petrosian
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  4. A. Zhadan
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  6. A. Martemyanov
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Correspondence to O. Petrosian.

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This work was supported by Saint Petersburg State University (project ID: 94062114).

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Dolgintseva, E., Wu, H., Petrosian, O. et al. Comparison of multi-step forecasting methods for renewable energy. Energy Syst (2024). https://doi.org/10.1007/s12667-024-00656-w

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