Recurrent Neural Network Based Model Development for Energy Consumption Forecasting

Yıl 2022, , 759 - 769, 30.09.2022

Halit Çetiner

https://doi.org/10.17798/bitlisfen.1077393

Öz

The world population is increasing day by day. As a result, limited resources are decreasing day by day. On the other hand, the amount of energy needed is constantly increasing. In this sense, decision makers must accurately estimate the amount of energy that society will require in the coming years and make plans accordingly. These plans are of critical importance for the peace and welfare of society. Based on the energy consumption values of Germany, it is aimed at estimating the energy consumption values with the GRU, LSTM, and proposed hybrid LSTM-GRU methods, which are among the popular RNN algorithms in the literature. The estimation performances of LSTM and GRU algorithms were obtained for MSE, RMSE, MAPE, MAE, and R2 values as 0.0014, 0.0369, 6.35, 0.0292, 0.9703 and 0.0017, 0.0375, 6.60, 0.0298, 0.9650, respectively. The performance of the proposed hybrid LSTM-GRU method, which is another RNN-based algorithm used in the study, was obtained as 0.0013, 0.0358, 5.89, 0.0275, and 0.9720 for MSE, RMSE, MAPE, MAE and R2 values, respectively. Although all three methods gave similar results, the training times of the proposed hybrid LSTM-GRU and LSTM algorithms took 7.50 and 6.58 minutes, respectively, but it took 4.87 minutes for the GRU algorithm. As can be understood from this value, it has been determined that it is possible to obtain similar values by sacrificing a very small amount of prediction performance in cases with time limitations.

Anahtar Kelimeler

GRU, LSTM, Forecasting of energy consumption, RNN

Kaynakça

• B. Becerik-Gerber, M. Siddiqui, L. Birilakis, O. E-Anwar, N. El-Gohary, T. Mahfouz, G. Jog, S. Li, and A. Kandil, “Civil engineering grand challenges: Opportunities for data sensing, information analysis, and knowledge discovery” J. Comput. Civ. Eng., vol. 28, no. 4, 2014.
• A. S. Ahmad, M.Y. Hassan, H. A. Rahman, F. Hussin, H. Abdullah, and R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting” Renew. Sustain. Energy Rev., vol. 33, pp. 102–109, 2014, doi: https://doi.org/10.1016/j.rser.2014.01.069.
• K. Amasyali and N. M. El-Gohary, “A review of data-driven building energy consumption prediction studies” Renew. Sustain. Energy Rev., vol. 81, pp. 1192–1205, 2018, doi: https://doi.org/10.1016/j.rser.2017.04.095.
• M. Koengkan, J. A. Fuinhas, and N. Silva, “Exploring the capacity of renewable energy consumption to reduce outdoor air pollution death rate in Latin America and the Caribbean region” Environ. Sci. Pollut. Res., vol. 28, no. 2, pp. 1656–1674, 2021.
• M. Moreno, B. Úbeda, A. Skarmeta, and M. Zamora, “How can We Tackle Energy Efficiency in IoT BasedSmart Buildings?” Sensors, vol. 14, no. 6, pp. 9582–9614, May 2014, doi: 10.3390/s140609582.
• M. E. Günay, “Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey” Energy Policy, vol. 90, pp. 92–101, 2016.
• H. Zhong, J. Wang, H. Jia, Y. Mu, and S. Lv, “Vector field-based support vector regression for building energy consumption prediction” Appl. Energy, vol. 242, pp. 403–414, 2019, doi: https://doi.org/10.1016/j.apenergy.2019.03.078.
• J.-S. Chou, D.-N. Truong, and C.-C. Kuo, “Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning” Energy, vol. 224, 2021, doi: https://doi.org/10.1016/j.energy.2021.120100.
• N. H. A. Bahar, L. Michaela, S. Made, and V. V. Josh, “Meeting the food security challenge for nine billion people in 2050: What impact on forests?” Glob. Environ. Chang., vol. 62, May 2020, doi: 10.1016/j.gloenvcha.2020.102056.
• T. Huo, H. Ren, X. Zhang, W. Cai, W. Feng, N. Zhou, and X. Wang, “China’s energy consumption in the building sector: A Statistical Yearbook-Energy Balance Sheet based splitting method” J. Clean. Prod., vol. 185, pp. 665–679, 2018, doi: https://doi.org/10.1016/j.jclepro.2018.02.283.
• Y. Zhang, C.-Q. He, B.-J. Tang, and Y.-M. Wei, “China’s energy consumption in the building sector: A life cycle approach” Energy Build., vol. 94, pp. 240–251, 2015, doi: https://doi.org/10.1016/j.enbuild.2015.03.011.
• F. Raucent, “Western Europe Power Consumption” Entsoe Transparency Platform, 2022. [Online]. Available: https://transparency.entsoe.eu/. [Accessed: May. 12, 2022].
• H. Çetiner and B. Kara, “Recurrent Neural Network Based Model Development for Wheat Yield Forecasting” J. Eng. Sci. Adiyaman Univ., vol. 9, no. 16, pp. 204–218, 2022, doi: 10.54365/adyumbd.1075265.
• J. Wang, Y. Zhang, L.-C. Yu, and X. Zhang, “Contextual sentiment embeddings via bi-directional GRU language model” Knowledge-Based Syst., vol. 235, 2022, doi: 10.1016/j.knosys.2021.107663.
• L. Hu, C. Wang, Z. Ye, and S. Wang, “Estimating gaseous pollutants from bus emissions: A hybrid model based on GRU and XGBoost” Sci. Total Environ., vol. 783, 2021.
• F. Informatik, Y. Bengio, P. Frasconi, and J. Schmidhuber, “Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies” A F. Guid. to Dyn. Recurr. Neural Networks, Mar. 2003.
• H. Çetiner and İ. Çetiner, “Analysis of Different Regression Algorithms for the Estimate of Energy Consumption” Eur. J. Sci. Technol., no. 31, pp. 23–33, Dec. 2021, doi: 10.31590/ejosat.969539.
• Z. Pang, F. Niu, and Z. O’Neill, “Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons” Renew. Energy, vol. 156, pp. 279–289, 2020, doi: https://doi.org/10.1016/j.renene.2020.04.042.
• D. T. Çetin and S. Metlek, “Forecasting of Turkish sovereign sukuk prices using artificial neural network model” Acta Infologica, vol. 5, no. 2, pp. 241–254, 2021.
• J. Q. Wang, Y. Du, and J. Wang, “LSTM based long-term energy consumption prediction with periodicity,” Energy, vol. 197, 2020.
• M. Demircioğlu and S. Eşiyok, “Energy consumption forecast of Turkey using artificial neural networks from a sustainability perspective” Int. J. Sustain. Energy, pp. 1–15, Jan. 2022, doi: 10.1080/14786451.2022.2026357.
• M. Bilgili, N. Arslan, A. Şekertekin, and A. Yaşar, “Application of long short-term memory (LSTM) neural network based on deep learning for electricity energy consumption forecasting” Turkish J. Electr. Eng. Comput. Sci., vol. 30, no. 1, pp. 140–157, 2022.
• P. C. Albuquerque, D. O. Cajueiro, and M. D. C. Rossi, “Machine learning models for forecasting power electricity consumption using a high dimensional dataset” Expert Syst. Appl., vol. 187, 2022, doi: https://doi.org/10.1016/j.eswa.2021.115917.
• S. Ranjit, S. Shrestha, S. Subedi, and S. Shakya, “Comparison of algorithms in foreign exchange rate prediction” in 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), pp. 9–13, 2018.
• M. Kowsher, A. Tahabilder, Md. Z. I. Sanjid, N. J. Prottasha, Md. S. Uddin, Md. A. Hossain, Mc. A. K. Jilani, “LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy” Procedia Comput. Sci., vol. 193, pp. 131–140, 2021, doi: https://doi.org/10.1016/j.procs.2021.10.013.
• Y. Wei, X. Zhang, Y. Shi, L. Xia, S. Pan, J. Wu, M. Han, and X. Zhao, “A review of data-driven approaches for prediction and classification of building energy consumption” Renew. Sustain. Energy Rev., vol. 82, pp. 1027–1047, 2018.
• S. García, A. Parejo, E. Personal, J. I. Guerrero, F. Biscarri, and C. León, “A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level” Appl. Energy, vol. 287, 2021.
• K. E. ArunKumar, D. V Kalaga, C. M. S. Kumar, M. Kawaji, and T. M. Brenza, “Comparative analysis of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells, Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA) for forecasting COVID-19 trends” Alexandria Eng. J., 2022.
• E. Ahmadzadeh, H. Kim, O. Jeong, N. Kim, and I. Moon, “A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification” IEEE Access, 2022.
• S. S. Bhadouria and S. Gupta, “Combined LSTM GRU Model for Prediction of Congestion State in QUIC Protocol” in Proceedings of International Conference on Computational Intelligence and Emerging Power System, pp. 123–131, 2022.
• W. Li, H. Wu, N. Zhu, Y. Jiang, J. Tan, and Y. Guo, “Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)” Inf. Process. Agric., vol. 8, no. 1, pp. 185–193, 2021.
• H. Çetiner, “Yaprak Hastalıklarının Sınıflandırılabilmesi İçin Önceden Eğitilmiş Ağ Tabanlı Sinir Ağı Geliştirimi” Adıyaman Üniversitesi Mühendislik Bilim. Derg., vol. 15, pp. 442–456, Sep. 2021, doi: 10.54365/adyumbd.988049.