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Research Article

Short-term electricity consumption forecasting with NARX, LSTM, and SVR for a single building: small data set approach

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Pages 6898-6908 | Received 14 Dec 2021, Accepted 09 Jul 2022, Published online: 26 Jul 2022

References

  • Ahmed, R., V. Sreeram, Y. Mishra, and M. D. Arif. 2020. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renewable and Sustainable Energy Reviews 124:109792. doi:10.1016/j.rser.2020.109792.
  • Bianchi, F. M., E. Maiorino, M. C. Kampffmeyer, A. Rizzi, and R. Jenssen. 2018. An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting. Springer International Publishing 1–41. doi:10.1007/978-3-319-70338-1.
  • Borowski, M., and K. Zwolińska. 2020. Prediction of cooling energy consumption in hotel building using machine learning techniques. Energies 13:6226. doi:10.3390/en13236226.
  • Bourdeau, M., X. Q. Zhai, E. Nefzaoui, X. Guo, and P. Chatellier. 2019. Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48:101533. doi:10.1016/j.scs.2019.101533.
  • Buitrago, J., and S. Asfour. 2017. Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs. Energies 10 (1):1–24. doi:10.3390/en10010040.
  • Casteleiro-Roca, J. L., J. Gómez-González, J. Calvo-Rolle, E. Jove, H. Quintián, B. Gonzalez Diaz, and J. Mendez Perez. 2019. Short-term energy demand forecast in hotels using hybrid intelligent modelling. Sensors 19:18. doi:10.3390/s19112485.
  • Cholewa, T., A. Siuta-Olcha, A. Smolarz, P. Muryjas, P. Wolszczak, Ł. Guz, and C. A. Balaras. 2021. On the short term forecasting of heat power for heating of building. Journal of Cleaner Production 307:127232. doi:10.1016/j.jclepro.2021.127232.
  • Dong, W., H. Sung, J. Tan, L. Zheng, J. Zhang, and Y. Y. Zhao. 2021. Short-term regional wind power forecasting for small datasets with input data correction, hybrid neural network, and error analysis. Energy Reports 7:7675–92. doi:10.1016/j.egyr.2021.11.021.
  • European Commission. 2020. ‘In focus: Energy efficiency in buildings,’ 17th of February. Accessed 10 November 2021. https://ec.europa.eu/info/news/focus-energy-efficiency-buildings-2020-lut-17_en
  • Frieden, D., J. Roberts, and A. F. Gubina. 2019. Overview of emerging regulatory frameworks on collective self-consumption and energy communities in Europe. Int Conf Eur Energy Mark EEM. doi:10.1109/EEM.2019.8916222.
  • Gao, G., K. Lo, and F. Fan. 2016. Comparison of ARIMA and ANN approaches in time-series predictions of traffic noise. Noise Control Engineering 64. doi:10.4236/epe.2017.94b015.
  • Guo, X., Q. Zhao, S. Wang, D. Shan, and W. Gong. 2021. A short-term load forecasting model of LSTM neural network considering demand response. Complexity 2021:7. doi:10.1155/2021/5571539.
  • Katsatos, A. L., and K. P. Moustris. 2019. Application of artificial neuron networks as energy consumption forecasting tool in the building of regulatory authority of energy, Athens, Greece. Elsevier 157:851–61. doi:10.1016/j.egypro.2018.11.251.
  • Klein, A., S. Falkner, S. Bartels, P. Hennig, and F. Hutter. Fast Bayesian optimization of machine learning hyperparameters on large datasets. presented at the 20th International Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, USA, 2017. doi: 10.48550/arXiv.1605.07079.
  • Koschwitz, D., J. Frisch, and C. Treeck. 2018. Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX recurrent neural network: A comparative study on district scale. Energy 165:134–42. doi:10.1016/j.energy.2018.09.068.
  • Lauricella, M., Z. Cal, and L. Fagiano. 2020. Day-ahead building load forecasting with a small dataset. IFAC-PapersOnLine 53:13076–81. doi:10.1016/j.ifacol.2020.12.2257.
  • Luo, X. J., L. O. Oyedele, A. O. Ajayi, C. G. Monyei, O. O. Akinade, and L. A. Akanbi. 2019. Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands. Advanced Engineering Informatics 41:100926. doi:10.1016/j.aei.2019.100926.
  • Luo, X. J., and L. O. Oyedele. 2021. Forecasting building energy consumption: adaptive long-short term memory neural networks driven by genetic algorithm. Advanced Engineering Informatics 50:101357. doi:10.1016/j.aei.2021.101357.
  • Ozbek, A., A. Yildirim, and M. Bilgili. 2021. Deep learning approach for one-hour ahead forecasting of energy production in a solar-PV plant. Energy Sources, Part A: Recovery, Utilization and Environmental Effects. doi:10.1080/15567036.2021.1924316.
  • Pallonetto, F., C. Jin, and E. Mangina. 2022. Forecast electricity demand in commercial building with machine learning models to enable demand response programs. Energy and AI 7:100121. doi:10.1016/j.egyai.2021.100121.
  • Smola, A. J., and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14 (3):199–222. doi:10.1023/B:STCO.0000035301.49549.88.
  • Ullah, F. U. M., A. Ullah, I. U. Haq, S. Rho, and S. W. Baik. 2020. Short-term prediction of residential power energy consumption via CNN and Multi-Layer Bi-Directional LSTM networks. Electric Power Systems Research 8. doi:10.1109/ACCESS.2019.2963045.
  • Wang, W., T. Hong, X. Xu, J. Chen, Z. Liu, and N. Xu. 2019. Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm. Applied Energy 248:217–30. doi:10.1016/j.apenergy.2019.04.085.

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