Univariate deep learning models for short-term electricity load forecasting from renewables

Year 2025, Volume: 74 Issue: 4, 670 - 686, 24.12.2025

Fatma Başoğlu Kabran , Kamil Demirberk Ünlü

https://doi.org/10.31801/cfsuasmas.1643466

https://izlik.org/JA49NF68HM

Abstract

Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics—mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination—are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an $R^2$ of almost $94\%$. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.

Keywords

Deep learning , univariate model , renewable energy , short-term load forecasting

References

• Ahmad, T., Zhang, H., Yan, B., A review on renewable energy and electricity requirement forecasting models for smart grid and buildings, Sustainable Cities and Society, 55 (2020), 102052. https://doi.org/10.1016/j.scs.2020.102052.
• Akbal, Y., Ünlü, K. D., A deep learning approach to model daily particular matter of Ankara: Key features and forecasting, International Journal of Environmental Science and Technology, 19(7) (2022), 1-17. https://doi.org/10.1007/s13762-021-03730-3.
• Akbal, Y., Ünlü, K. D., A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production, Renewable Energy, 200 (2022), 832-844. https://doi.org/10.1016/j.renene.2022.10.055.
• Akça, E., Yozgatlıgil, C., Mutual information model selection algorithm for time series, Journal of Applied Statistics, 47(12) (2020), 2192-2207. https://doi.org/10.1080/02664763.2019.1707516.
• Akman, T., Yılmaz, C., Sönmez, Y., Short-term electric energy load forecasting of Ankara Region using artificial intelligence methods, Politeknik Dergisi, 26(4) (2023), 1517-1531. https://doi.org/10.2339/politeknik.911634.
• Alfares, H. K., Nazeeruddin, M., Electric load forecasting: literature survey and classification of methods, International journal of systems science, 33(1) (2002), 23-34. https://doi.org/10.1080/00207720110067421.
• Bilgiç, M., Girep, C. P., Aslanoğlu, S. Y., Aydinalp-Koksal, M., Forecasting Turkey’s short term hourly load with artificial neural networks, In IEEE PES T&D 2010, IEEE, (2010), 1-7. https://doi.org/10.1109/TDC.2010.5484442.
• Bozkurt, Ö. Ö., Biricik, G., Taysi, Z. C., Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market, PloS one, 12(4) (2017), e0175915. https://doi.org/10.1371/journal.pone.0175915.
• Chen, Y., Zhang, D., Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory, Advances in Applied Energy, 1 (2021), 100004. https://doi.org/10.1016/j.adapen.2020.100004.
• 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 preprint arXiv:1406.1078, (2014). https://doi.org/10.48550/arXiv.1406.1078.
• Çevik, H. H., Çunkaş, M., A fuzzy logic based short term load forecast for the holidays, International Journal of Machine Learning and Computing, 6(1) (2016), 57. https://doi.org/10.18178/ijmlc.2016.6.1.572.
• Çevik, H. H., Çunkaş, M., Short-term load forecasting using fuzzy logic and ANFIS, Neural Computing and Applications, 26(6) (2015), 1355-1367. https://doi.org/10.1007/s00521-014-1809-4.
• Dedinec, A., Filiposka, F., Dedinec, A., Kocarev, L., Deep belief network based electricity load forecasting: An analysis of Macedonian case, Energy, 115 (2016), 1688-1700. https://doi.org/10.1016/j.energy.2016.07.090.
• Dutta A., Kumar, S., Basu, M., A gated recurrent unit approach to bitcoin price prediction, Journal of Risk and Financial Management, 13(2) (2020), 23. https://doi.org/10.3390/jrfm13020023.
• EMBER, Türkiye Electricity Review 2024. Accessed on September, 2024. https://ember-climate.org/insights/research/turkiye-electricity-review-2024
• EPDK, Energy Market Regulatory Authority Natural Gas Market 2023 Sector Report. Accessed on July, 2024. https://www.epdk.gov.tr/Detay/DownloadDocument?id=tygdmUeMvuI
• EPDK, Energy Market Regulatory 2023 Activity Report. Accessed on October, 2024. https://www.epdk.gov.tr/Detay/Icerik/5-15619/energy-market-regulatory-authority-activity-report
• EPİAŞ, Seffaflık Platformu. Accessed on February, 2024. https://seffaflik.epias.com.tr/transparency/uretim/gerceklesen-uretim/gercek-zamanli-uretim.xhtml
• Erkmen, I., Özdoğan, A., Short term load forecasting using genetically optimized neural network cascaded with a modified Kohonen clustering process, In Proceedings of 12th IEEE International Symposium on Intelligent Control, IEEE, (1997), 107-112. https://doi.org/10.1109/ISIC.1997.626422.
• Evkaya, O. O., Yozgatlıgıl, C., Selcuk-Kestel, A. S., Measuring Dependence between Electricity Consumption and Contributing Indicators via Copulas: Turkish Case, Gazi University Journal of Science, 31(4) (2018), 1284-1296.
• Fallah, S. N., Ganjkhani, M., Shamshirband, S., Chau, K. W., Computational intelligence on short-term load forecasting: A methodological overview, Energies, 12(3) (2019), 393. https://doi.org/10.3390/en12030393.
• Gao, X., Li, X., Zhao, B., Ji, W., Jing, X., He, Y., Short-term electricity load forecasting model based on EMD-GRU with feature selection, Energies, 12(6) (2019), 1140. https://doi.org/10.3390/en12061140.
• Gasparin, A., Lukovic, S., Alippi, C., Deep learning for time series forecasting: The electric load case, CAAI Transactions on Intelligence Technology, 7(1) (2022), 1-25. https://doi.org/10.1049/cit2.12060.
• Ghofrani, M., Ghayekhloo, M., Arabali, A., Ghayekhloo, A., A hybrid short-term load forecasting with a new input selection framework, Energy, 81 (2015), 777-786. https://doi.org/10.1016/j.energy.2015.01.028.
• Gökgöz, F., Filiz, F., Electricity load forecasting via ANN approach in Turkish electricity markets, Bilgi Yönetimi, 3(2) (2020), 170-184. https://doi.org/10.33721/by.834285.
• Hammad, M. A., Jereb, B., Rosi, B., Dragan, D., Methods and models for electric load forecasting: a comprehensive review, Logistics, Supply Chain, Sustainability and Global Challenges, 11(1) (2020), 51-76. https://doi.org/10.2478/jlst-2020-0004.
• Hochreiter, S., Schmidhuber, J., Long short-term memory, Neural computation, 9(8) (1997), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735.
• Hong, T., Fan, S., Probabilistic electric load forecasting: A tutorial review, International Journal of Forecasting, 32(3) (2016), 914-938. https://doi.org/10.1016/j.ijforecast.2015.11.011.
• Hou, H., Liu, C., Wang, Q., Wu, X., Tang, J., Shi, Y., Xie, C., Review of load forecasting based on arti- ficial intelligence methodologies, models, and challenges, Electric Power Systems Research, 210 (2022), 108067. https://doi.org/10.1016/j.epsr.2022.108067.
• Hua, H., Liu, M., Li, Y., Deng, S., Wang, Q., An ensemble framework for short-term load forecasting based on parallel CNN and GRU with improved ResNet, Electric Power Systems Research, 216 (2023), 109057. https://doi.org/10.1016/j.epsr.2022.109057.
• Investment Office, Presidency of the Republic of Türkiye, Energy Industry Report 2023. Accessed on November, 2024. https://www.invest.gov.tr/en/library/publications/lists/investpublications/energy-industry.pdf
• Khan, A. R., Mahmood, A., Safdar, A., Khan, Z. A., Khan, N. A., Load forecasting, dynamic pricing and DSM in smart grid: A review, Renewable and Sustainable Energy Reviews, 54 (2016), 1311-1322. https://doi.org/10.1016/j.rser.2015.10.117.
• Kuster, C., Rezgui, Y., Mourshed, M., Electrical load forecasting models: A critical systematic review, Sustainable cities and society, 35 (2017), 257-270. https://doi.org/10.1016/j.scs.2017.08.009.
• Kwon, B. S., Park, R. J., Song, K. B., Short-term load forecasting based on deep neural networks using LSTM layer,Journal of Electrical Engineering & Technology, 15(4) (2020), 1501-1509. https://doi.org/10.1007/s42835-020-00424-7.
• LeCun, Y., Bengio, Y., Hinton, G., Deep learning, Nature, 521(7553) (2015), 436-444. https://doi.org/10.1038/nature14539.
• Livieris, I. E., Pintelas, E., Pintelas, P., A CNN–LSTM model for gold price time-series forecasting, Neural computing and applications, 32(23) (2020), 17351-17360. https://doi.org/10.1007/s00521-020-04867-x.
• Lu, W., Li, J., Wang, J., Qin, L., A CNN-BiLSTM-AM method for stock price prediction, Neural Computing and Applications, 33(10) (2021), 4741-4753. https://doi.org/10.1007/s00521-020-05532-z.
• Mishra, M., Nayak, J., Naik, B., Abraham, A., Deep learning in electrical utility industry: A comprehensive review of a decade of research, Engineering Applications of Artificial Intelligence, 96 (2020), 104000. https://doi.org/10.1016/j.engappai.2020.104000.
• Nalcaci, G., Özmen, A., Weber, G. W., Long-term load forecasting: models based on MARS, ANN and LR methods, Central European Journal of Operations Research, 27 (2019), 1033-1049. https://doi.org/10.1007/s10100-018-0531-1.
• Nti, I. K., Teimeh, M., Nyarko-Boateng, O., Adekoya, A. F., Electricity load forecasting: a systematic review, Journal of Electrical Systems and Information Technology, 7(1) (2020), 1-19. https://doi.org/10.1186/s43067-020-00021-8.
• OECD, The Climate Action Monitor 2023: Providing Information to Monitor Progress Towards Net-Zero, OECD Publishing, Paris, 2023. https://doi.org/10.1787/60e338a2-en.
• Ozdemir, O., Yozgatligil, C., Forecasting performance of machine learning, time series, and hybrid methods for low-and high-frequency time series, Statistica Neerlandica, 78(2) (2024), 441-474. https://doi.org/10.1111/stan.12326.
• Öz Bakan, H., Yilmaz, F., Weber, G. W., An efficient algorithm for stochastic optimal control problems by means of a least-squares Monte-Carlo method, Optimization, 71(11) (2022), 3133-3146. https://doi.org/10.1080/02331934.2021.2009824.
• Özcanlı, A. K., Yaprakdal, F., Baysal, M., Deep learning methods and applications for electrical power systems: A comprehensive review, International Journal of Energy Research, 44(9) (2020), 7136-7157. https://doi.org/10.1002/er.5331.
• Özkurt N., Öztura, H., Güzeliş, C., Electricity energy forecasting for Turkey: A review of the years 2003–2020, Turk J Electr Power Energy Syst., 1(2) (2021), 118-128. https://doi.org/10.5152/tepes.2021.21019.
• Özmen, A., Kropat, E., Weber, G. W., Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty, Optimization, 66(12) (2017), 2135-2155. https://doi.org/10.1080/02331934.2016.1209672.
• Rafi, S. H., Deeba, S. R., Hossain, E., A short-term load forecasting method using integrated CNN and LSTM network, IEEE Access, 9 (2021), 32436-32448. https://doi.org/10.1109/ACCESS.2021.3060654.
• Rawat, W., Wang, Z., Deep convolutional neural networks for image classification: A comprehensive review, Neural computation, 29(9) (2017), 2352-2449. https://doi.org/10.1162/necoa00990.
• Raza, M. Q., Khosravi, A., A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings, Renewable and Sustainable Energy Reviews, 50 (2015), 1352-1372. https://doi.org/10.1016/j.rser.2015.04.065.
• Ritchie, H., Roser, M., Rosado, P., $CO_2$ and greenhouse gas emissions. (2020). https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions.
• Saeed, F., Paul, A., Seo, H., A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting, Energies, 15(6) (2022), 2263. https://doi.org/10.3390/en15062263.
• Schmidhuber, J., Learning complex, extended sequences using the principle of history compression, Neural Computation, 4(2) (1992), 234-242. https://doi.org/10.1162/neco.1992.4.2.234.
• Tang, X., Chen, H., Xiang, W., Yang, J., Zou, M., Short-term load forecasting using channel and temporal attention based temporal convolutional network, Electric Power Systems Research, 205 (2022), 107761. https://doi.org/10.1016/j.epsr.2021.107761.
• Taylan, P., Weber, G. W., Yerlikaya- Özkurt, F., A new approach to multivariate adaptive regression splines by using Tikhonov regularization and continuous optimization, Top (The Operational Research Journal of SEIO (Spanish Statistics and Operations Research Society)), 18(2) (2010), 377-395. https://doi.org/10.1007/s11750-010-0155-7.
• TEİAŞ, Accessed on August 2024. https://www.teias.gov.tr/tr-TR/turkiye-elektrik-uretim-iletim-istatistikleri.
• Tokgöz, A., Ünal, G., A RNN based time series approach for forecasting Turkish electricity load, In 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, (2018), 1-4. https://doi.org/10.1109/SIU.2018.8404313.
• Topallı, A. K., Erkmen, I., A hybrid learning for neural networks applied to short term load forecasting, Neurocomputing, 51 (2003), 495-500. https://doi.org/10.1016/S0925-2312(02)00870-6.
• Topallı, A. K., Erkmen, I., Topallı, I., Intelligent short-term load forecasting in Turkey, International Journal of Electrical Power & Energy Systems, 28(7) (2006), 437-447. https://doi.org/10.1016/j.ijepes.2006.02.004.
• Ünlü, K. D., A data-driven model to forecast multi-step ahead time series of Turkish daily electricity load, Electronics, 11(10) (2022), 1524. https://doi.org/10.3390/electronics11101524.
• Vanting, N. B., Ma, Z., Jørgensen, B. N., A scoping review of deep neural networks for electric load forecasting, Energy Informatics, 4(2) (2021), 1-13. https://doi.org/10.1186/s42162-021-00148-6.
• Wang, H., Lei, Z., Zhang, X., Zhou, B., Peng, J., A review of deep learning for renewable energy forecasting, Energy Conversion and Management, 198 (2019), 111799. https://doi.org/10.1016/j.enconman.2019.111799.
• Weber, G. W., Taylan, P., Alparslan-Gök, S. Z., Özöğür-Akyüz, S., Akteke- Öztürk, B., Optimization of gene- environment networks in the presence of errors and uncertainty with Chebychev approximation, Top, 16 (2008), 284-318. https://doi.org/10.1007/s11750-008-0052-5.
• Yazıcı, I., Beyca, O. F., Delen, D., Deep-learning-based short-term electricity load forecasting: A real case application, Engineering Applications of Artificial Intelligence, 109 (2022), 104645. https://doi.org/10.1016/j.engappai.2021.104645.
• Yıldırım, M. H., Bayrak, Ö. T., Weber, G. W., Survey and evaluation on modelling of next-day electricity prices. In Modeling, Dynamics, Optimization and Bioeconomics I: Contributions from ICMOD 2010 and the 5th Bioeconomy Conference 2012. Springer International Publishing, (2014), 723-737.
• Yılmaz, Y., Nalçacı, G., Kańczurzewska, M., Weber, G. W., Long-term wind power and global warming prediction using MARS, ANN, CART, LR, and RF, Journal of Industrial and Management Optimization, 20(6) (2024), 2193-2216. https://doi.org/10.3934/jimo.2023162.
• Zhang, D., Han, X., Deng, C., Review on the research and practice of deep learning and reinforcement learning in smart grids, CSEE Journal of Power and Energy Systems, 4(3) (2018), 362-370. https://doi.org/10.17775/CSEEJPES.2018.00520.
• Zhang Y. G., Tang, J., He, Z. Y., Tan, J., Li, C., A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide, Natural Hazards, 105 (1) (2021), 783-813. https://doi.org/10.1007/s11069-020-04337-6.