Comparison of Robust Machine-learning and Deep-learning Models for Midterm Electrical Load Forecasting

Yıl 2022, Cilt: 12 Sayı: 2, 102 - 107, 30.12.2022

Fatma Yaprakdal Fatih Bal

https://doi.org/10.36222/ejt.1201977

Öz

Electrical load forecasting (ELF) is gaining importance especially due to the severe impact of climate change on electrical energy usage and dynamically evolving smart grid technologies in the last decades. In this regard, medium-term load forecasting, a crucial need for power system planning (generation optimization and outages plan) and operation control, has become prominent in particular. Machine learning and deep learning-based techniques are currently trending approaches in electrical load estimation due to their capability to model complex non-linearity, feature abstraction and high accuracy, especially in the smart power systems environment. In this study, several load forecasting models based on machine learning methods which comprise linear regression (LR), decision tree (DT), random forest (RF), gradient boosting, adaBoost, and deep learning techniques such as recurrent neural network (RNN) and long short-term memory (LSTM) are studied for medium-term electrical load demand forecasting at an aggregated level. Performance metric results of these analyzes are presented in detail. State-of-the-art feature selection models are examined on the dataset and their effects on these forecasting methods are evaluated. Numerical results show that forecasting performance can be significantly improved. These results are validated by the results of other studies on the subject and found to be superior.

Anahtar Kelimeler

Medium-term load forecasting, Machine learning, Deep learning, Aggregated-level forecasting, Feature selection

Kaynakça

• [1] S. Avdaković , E. Bećirović , N. Hasanspahić , M. Musić , A. Merzić , A. Tuhčić , J. Karadža , D. Pešut ve A. K. Lončarević , "Long-term forecasting of energy, electricity and active power demand – Bosnia and Herzegovina case study", Balkan Journal of Electrical and Computer Engineering, 3, 1, 2015, pp. 11-16,
• [2] I. Samuel, C. Felly-Njoku, A. Adewale, A. Awelewa, “Medium-term load forecasting of covenant university using the regression analysis methods,” Journal of Energy Technologies and Policy 4, 2014.
• [3] A. Zare-Noghabi, M. Shabanzadeh, H. Sangrody, “Medium-term load forecasting using support vector regression, feature selection, and symbiotic organism search optimization,” IEEE Power & Energy Society General Meeting (PESGM), 2019, pp. 1–5.
• [4] L. Han, Y Peng, Y. Li, B. Yong, Q. Zhou et al, “Enhanced deep networks for short-term and medium-term load forecasting,” IEEE Access, 2019, pp. 4045–4055. [5] M. Ghiassi, D.K. Zimbra, H. Saidane, “Medium term system load forecasting with a dynamic artificial neural network model,” Electric Power Systems Research 76, 5, 2006, pp. 302–316.
• [6] O. Ozgonenel ve A. Gözüoğlu , "Fuzzy Logic Based Smart Home Automation and Forecasting Electric Energy Consumption", Balkan Journal of Electrical and Computer Engineering, 9, 4, 2021, pp. 365-370, doi:10.17694/bajece.928537
• [7] M. Erkınay Özdemir , "Yapay Sinir Ağları Kullanılarak Orta Dönem Elektrik Enerjisi Tüketim Tahmini: İskenderun Örneği", Avrupa Bilim ve Teknoloji Dergisi, no. 28, pp. 489-492, Nov. 2021, doi:10.31590/ejosat.1007589
• [8] G. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung, “Time Series Analysis: Forecasting and Control,” 5th ed. John Wiley and Sons Inc., 2015.
• [9] M. Braun, H. Altan, S. Beck, “Using regression analysis to predict the future energy consumption of a supermarket in the uk,” Applied Energy 130, 2014, pp. 305–313.
• [10] M. Yılmaz , "Real Measure of a Transmission Line Data with Load Fore-cast Model for The Future", Balkan Journal of Electrical and Computer Engineering, 6, 2, 2018, pp. 141-145, doi:10.17694/bajece.419646
• [11] R. Torkzadeh, A. Mirzaei, M.M. Mirjalili, A.S. Anaraki, M.R. Sehhati et al, “Medium term load forecasting in distribution systems based on multi linear regression & principal component analysis: A novel approach,” 19th Conference on Electrical Power Distribution Networks (EPDC), 2014, pp. 66–70.
• [12] A. Papalexopoulos, T. Hesterberg, “A regression-based approach to short-term system load forecasting,” IEEE Transactions on Power Systems 5, 4, 1990, pp. 1535–1547.
• [13] A. Danandeh Mehr , F. Bagheri and M. J. S. Safari , "Electrical Energy Demand Prediction: A Comparison Between Genetic Programming and Decision Tree", Gazi University Journal of Science, vol. 33, no. 1, pp. 62-72, Mar. 2020, doi:10.35378/gujs.554463
• [14] Z. Wang, Y. Wang, R. Zeng, R.S. Srinivasan, S. Ahrentzen, “Random forest based hourly building energy prediction,” Energy and Buildings 171, 2018, pp. 11–25.
• [15] A. Lahouar, J. Ben Hadj Slama, “Day-ahead load forecast using random forest and expert input selection” Energy Conversion and Management 103, 2015, 1040–1051.
• [16] M.W. Ahmad, M. Mourshed, Y. Rezgui, “Trees vs neurons: Comparison between random forest and ANN for high30 resolution prediction of building energy consumption” Energy and Buildings 147, 2017, pp. 77–89.
• [17] S. Wang, S. Wang, D. Wang, “Combined probability density model for medium term load forecasting based on quantile regression and kernel density estimation,” Energy Procedia 158, Innovative Solutions for Energy Transitions, 2019, pp. 6446–6451.
• [18] K. Zhu, J. Geng, K. Wang, “A hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting for holiday load forecasting” Electric Power Systems Research 190, 2021, 106841.
• [19] P.W. Khan, Y.C. Byun, S.J. Lee, D.H. Kang, J.Y. Kang et al, “Machine learning-based approach to predict energy consumption of renewable and nonrenewable power sources,” Energies 13, 2020, no. 18: 4870.
• [20] P.W. Khan, Y.C. Byun, “Genetic algorithm based optimized feature engineering and hybrid machine learning for effective energy consumption prediction” IEEE Access 8, 2020, pp. 196274–196286.
• [21] K.P. Waqas, Y.C. Byun, S.J. Lee, N. Park, “Machine learning based hybrid system for imputation and efficient energy demand forecasting,” Energies 13, 11, 2020.
• [22] K. Zeng, J. Liu, H. Wang, Z. Zhao, C. Wen, “Research on adaptive selection algorithm for multi-model load forecasting based on adaboost,” In IOP Conference Series: Earth and Environmental Science vol. 610, IOP Publishing, 2020, pp. 012005.
• [23] A. Rahman, V. Srikumar, A.D. Smith, “Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks,” Applied Energy 212, 2018, pp. 372–385.
• [24] S. Bouktif, A. Fiaz, A. Ouni, M.A. Serhani, “Single and multi-sequence deep learning models for short and medium term electric load forecasting,” Energies 12, 1, 2019.
• [25] S. Bouktif, A. Fiaz, A. Ouni, M.A. Serhani, “Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches,” Energies 11, 7, 2018.
• [26] T. Bashir, C. Haoyong, M.F. Tahir, Z. Liqiang, “Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN,” Energy Reports 8, 2022, pp. 1678–1686.
• [27] H.S. Nogay, T. C. Akinci, and M. Yilmaz. "Detection of invisible cracks in ceramic materials using by pre-trained deep convolutional neural network." Neural Computing and Applications 34.2 (2022): 1423-1432.
• [28] Y. Wang, N. Zhang, X. Chen, “A short-term residential load forecasting model based on LSTM recurrent neural network considering weather features,” Energies 14, 10, 2021.
• [29] C. Fan, Y. Li, L. Yi, L. Xiao, X. Qu et al, "Multi-objective LSTM ensemble model for household short-term load forecasting,” Memetic Computing 14, 2022, pp. 1–18.
• [30] Czech transmission system operator (Ceps) [online], Website https://www.cleanenergywire.org/experts/7, [accessed 22 08 22]
• [31] Nasa power data access viewer [online]. Website https://power.larc.nasa.gov/data-access-viewer [accessed 22 08 22]
• [32] Stanford University CS-230 Recurrent Neural Networks cheatsheet [online], Website https://stanford.edu/shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks [accessed 22 08 22].
• [33] W.J. Lee, J. Hong, "A hybrid dynamic and fuzzy time series model for mid-term power load forecasting,” International Journal of Electrical Power & Energy Systems 64, 2015, pp. 1057–1062.
• [34] G. Dudek, P. Pe lka, S. Smyl, “A hybrid residual dilated LSTM and exponential smoothing model for midterm electric load forecasting,” IEEE Transactions on Neural Networks and Learning Systems 33, 7, 2022, pp. 2879–2891.
• [35] S.M. Jung, S. Park , S.W. Jung, E. Hwang, “Monthly electric load forecasting using transfer learning for smart cities,” Sustainability 12, 16, 2020.
• [36] A. Samuel, M. Krishnamoorthy, B. Ananthan, K. Subramanian, K.P.Murugesan, “Application of metaheuristic algorithms for solving real-world electricity demand forecasting and generation expansion planning problems,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering 46, 2, 2022, pp. 413–439.