Open Access
| Issue |
RAIRO-Oper. Res.
Volume 60, Number 4, July-August 2026
AFROS2024-OR&AI
|
|
|---|---|---|
| Page(s) | 1959 - 1979 | |
| DOI | https://doi.org/10.1051/ro/2026054 | |
| Published online | 16 July 2026 | |
- P. Jiang, Z. Wang, X. Li, X.V. Wang, B. Yang and J. Zheng, Energy consumption prediction and optimization of industrial robots based on LSTM. J. Manuf. Syst. 70 (2023) 137–148. [Google Scholar]
- M. Sarkar, A. Majumder, S. Bhattacharya and B. Sarkar, Optimization of energy cycle under a sustainable supply chain management. RAIRO-Oper. Res. 57 (2023) 2177–2196. [Google Scholar]
- L. Wang, D. Xie, L. Zhou and Z. Zhang, Application of the hybrid neural network model for energy consumption prediction of office buildings. J. Build. Eng. 72 (2023) 106503. [Google Scholar]
- S. Tabasi, A. Aslani and H. Forotan, Prediction of energy consumption by using regression model. Comput. Res. Prog. Appl. Sci. Eng. 2 (2016) 110–115. [Google Scholar]
- O. Valgaev, F. Kupzog and H. Schmeck, Low-voltage power demand forecasting using k-nearest neighbors approach, in 2016 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia). IEEE (2016) 1019–1024. [Google Scholar]
- W. Hariri, H. Tabia, N. Farah, D. Declercq and A. Benouareth, Geometrical and visual feature quantization for 3D face recognition, in International Conference on Computer Vision Theory and Applications, Vol. 6. SciTePress (2017) 187–193. [Google Scholar]
- E. Elbeltagi and H. Wefki, Predicting energy consumption for residential buildings using ann through parametric modeling. Energy Rep. 7 (2021) 2534–2545. [Google Scholar]
- M. Ilbeigi, M. Ghomeishi and A. Dehghanbanadaki, Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm. Sustain. Cities Soc. 61 (2020) 102325. [Google Scholar]
- A. Mishra, H.R. Lone and A. Mishra, DECODE: Data-driven energy consumption prediction leveraging historical data and environmental factors in buildings. Energy Build. 307 (2024) 113950. [Google Scholar]
- M.M.S. Yazdan, M. Khosravia, S. Saki and M.A. Al Mehedi, Forecasting energy consumption time series using recurrent neural network in tensorflow. Technical Note (2022). [Google Scholar]
- S. Li and R. Li, Comparison of forecasting energy consumption in Shandong, China using the ARIMA model, GM model and ARIMA-GM model. Sustainability. 9 (2017) 1181. [Google Scholar]
- Z. Wang, Y. Wang, R. Zeng, R.S. Srinivasan and S. Ahrentzen, Random forest based hourly building energy prediction. Energy Build. 171 (2018) 11–25. [Google Scholar]
- A. Lotfipoor, S. Patidar and D.P. Jenkins, Short-term forecasting of residential electricity demand using CNN-LSTM, in 2nd IBPSA-Scotland uSIM Conference: Building to Buildings-Urban and Community Energy Modelling (2020). [Google Scholar]
- P. Geurts, D. Ernst and L. Wehenkel, Extremely randomized trees. Mach. Learn. 63 (2006) 3–42. [Google Scholar]
- S. Hochreiter and J. Schmidhuber, Long short-term memory. Neural Comput. 9 (1997) 1735–1780. [CrossRef] [PubMed] [Google Scholar]
- F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization 1958 (2021). [Google Scholar]
- F.M. de Souza, J. Grando and F. Baldo, Adaptive fast XGBoost for regression, in Brazilian Conference on Intelligent Systems. Springer, Berlin (2022) 92–106. [Google Scholar]
- T. Hastie, R. Tibshirani, J. Friedman, T. Hastie, R. Tibshirani and J. Friedman, Boosting and additive trees. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, Berlin (2009) 337–387. [Google Scholar]
- V.E. Balas, R. Kumar and R. Srivastava, Recent Trends and Advances in Artificial Intelligence and Internet of Things. Springer, Berlin (2020). [Google Scholar]
- R. Venkatesan and B. Li, Convolutional Neural Networks in Visual Computing: A Concise Guide. CRC Press (2017). [Google Scholar]
- L. Breiman, J. Friedman, R. Olshen and C. Stone, CART-Classification And Regression Trees. CRC Press (1984). [Google Scholar]
- G. Biau and E. Scornet, A random forest guided tour. Test 25 (2016) 197–227. [CrossRef] [Google Scholar]
- H. Zou and T. Hastie, Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301–320. [Google Scholar]
- L. Wang, J. Zhu and H. Zou, The doubly regularized support vector machine. Stat. Sin. 16 (2006) 589–615. [Google Scholar]
- M. Liu and B.C. Vemuri, A robust and efficient doubly regularized metric learning approach, in Computer Vision-ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV 12. Springer, Berlin (2012) 646–659. [Google Scholar]
- W. Shen, J. Wang and S. Ma, Doubly regularized portfolio with risk minimization, in Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28 (2014). [Google Scholar]
- P. Milanez-Almeida, A.J. Martins, R.N. Germain and J.S. Tsang, Cancer prognosis with shallow tumor RNA sequencing. Nat. Med. 26 (2020) 188–192. [Google Scholar]
- Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, M. Ringgaard and C.-J. Lin, Training and testing low-degree polynomial data mappings via linear SVM. J. Mach. Learn. Res. 11 (2010). [Google Scholar]
- J.D. Gergonne, The application of the method of least squares to the interpolation of sequences. Hist. Math. 1 (1974) 439–447. [Google Scholar]
- S.M. Stigler, Gergonne’s 1815 paper on the design and analysis of polynomial regression experiments. Hist. Math. 1 (1974) 431–439. [Google Scholar]
- K. Smith, On the standard deviations of adjusted and interpolated values of an observed polynomial function and its constants and the guidance they give towards a proper choice of the distribution of observations. Biometrika 12 (1918) 1–85. [Google Scholar]
- H. Drucker, C.J. Burges, L. Kaufman, A. Smola and V. Vapnik, Support vector regression machines, in Advances in Neural Information Processing Systems, Vol. 9. MIT Press (1996). [Google Scholar]
- A.J. Smola and B. Schölkopf, A tutorial on support vector regression. Stat. Comput. 14 (2004) 199–222. [Google Scholar]
- Kag dataset, https://www.kaggle.com/datasets/loveall/appliances-energy-prediction. Accessed: 2024-06-12. [Google Scholar]
- N.W. Azani, C.P. Trisya, L.M. Sari, H. Handayani and M.R.M. Alhamid, Performance comparison of ARIMA, LSTM and SVM models for electric energy consumption analysis. PREDATECS 1 (2024). [Google Scholar]
- M. Sachin, M. Paily Baby and A. Sudharson Ponraj, Analysis of energy consumption using RNN-LSTM and ARIMA model, in Journal of Physics: Conference Series, Vol. 1716. IOP Publishing (2020) 012048. [Google Scholar]
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