Adaptive Convolutional Residual Network for Dual-Task Forecasting in Energy Market Planning
Abstract
Accurate forecasting and strategic decision-making are critical for electricity market planning in the domain of energy informatics, where grid reliability, economic stability, and sustainability must be balanced. This paper introduces the Adaptive Convolutional Residual Network (ACRN), a dual-task deep learning architecture designed to jointly perform project risk classification and electricity price regression. The model is trained and evaluated on a real-world dataset from Frankfurt, Germany, comprising hourly records from 2018 to 2024 and including 12 core variables such as electricity price, market demand, renewable generation, and regulatory policies. ACRN integrates novel preprocessing techniques including weighted temporal interpolation, dynamic thresholding, and hybrid normalization, along with adaptive feature refinement and context-aware feature derivation. The framework achieves a classification accuracy of 98.5%, F1- score of 98.1%, AUC of 99.0%, and regression MAPE of 2.33% while reducing computational cost by 10% compared to baseline models. In comparison with state-of-the-art methods such as EfficientNet, WideResNet, and Gradient Boosting, ACRN outperforms all in both predictive accuracy and time efficiency. These results demonstrate ACRN’s robustness and scalability in addressing the multidimensional forecasting requirements of modern power markets. The proposed model offers a self-contained, high-performance solution for energy market planning with practical relevance for both policy and industry applications.References
Q. Hassan, P. Viktor, T. J. Al-Musawi, B. M. Ali, S. Algburi, H. M. Alzoubi, and M. Jaszczur, “The renewable energy role in the global energy transformations,” Renewable Energy Focus, vol. 48, pp. 100545, 2024.
C. Wu, W. Gu, E. Luo, X. Chen, H. Lu, and Z. Yi, “An economic cybernetic model for electricity market operation coupled with physical system dynamics,” Applied Energy, vol. 335, pp. 120764, 2023.
Q. Hassan, S. Algburi, A. Z. Sameen, H. M. Salman, and M. Jaszczur, “A review of hybrid renewable energy systems: Solar and wind-powered solutions: Challenges, opportunities, and policy implications,” Results in Engineering, pp. 101621, 2023.
Q. Hassan, S. Algburi, M. Jaszczur, P. Viktor, A. Iqbal, A. K. Al-Jiboory, and A. Z. Sameen, “Implement and evaluate resilient energy infrastructures capable of withstanding spatial, temporal, and annual weather fluctuations in Saudi Arabia by 2050,” Sustainable Futures, vol. 7, pp. 100182, 2024.
H. R. Nabil, R. Mamun, T. Nasir, K. N. S. Netu, A. Bhowmik, and D. Karmaker, “Harnessing deep learning for plant disease analysis: Current trends, challenges, and future prospects,” Challenges and Future Prospects.
M. Irfan, N. Ayub, F. Althobiani, S. Masood, Q. Arbab Ahmed, M. H. Saeed, and S. N. Faraj Mursal, “Ensemble learning approach for advanced metering infrastructure in future smart grids,” PLOS ONE, vol. 18, no. 10, pp. e0289672, 2023.
W. Atwa, A. A. Almazroi, and N. Ayub, “Reliable renewable energy forecasting for climate change mitigation,” PeerJ Computer Science, vol. 10, pp. e2067, 2024.
A. A. Almazroi and N. Ayub, “Multi-task learning for electricity price forecasting and resource management in cloud-based industrial IoT systems,” IEEE Access, vol. 11, pp. 54280-54295, 2023.
M. Aldossary, H. A. Alharbi, and N. Ayub, “Exploring multi-task learning for forecasting energy-cost resource allocation in IoT-cloud systems,” Computers, Materials & Continua, vol. 79, no. 3, 2024.
A. A. Almazroi and N. Ayub, “Nature-inspired approaches for clean energy integration in smart grids,” Alexandria Engineering Journal, vol. 105, pp. 640-654, 2024.
A. A. Almazroi, F. S. Alsubaei, N. Ayub, and N. Z. Jhanjhi, “Inclusive smart cities: IoT-cloud solutions for enhanced energy analytics and safety,” International Journal of Advanced Computer Science & Applications, vol. 15, no. 5, 2024.
S. F. Ahmed, M. S. B. Alam, M. Hassan, M. R. Rozbu, T. Ishtiak, N. Rafa, and A. H. Gandomi, “Deep learning modeling techniques: Current progress, applications, advantages, and challenges,” Artificial Intelligence Review, vol. 56, no. 11, pp. 13521-13617, 2023.
X. Li, Z. Wang, C. Yang, and A. Bozkurt, “An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms,” Energy, vol. 296, pp. 131259, 2024.
S. Ghimire, R. C. Deo, D. Casillas-Pérez, E. Sharma, S. Salcedo-Sanz, P. D. Barua, and U. R. Acharya, “Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach,” Applied Energy, vol. 374, pp. 123920, 2024.
M. D. Alanazi, A. Saeed, M. Islam, S. Habib, H. I. Sherazi, S. Khan, and M. M. Shees, “Enhancing short-term electrical load forecasting for sustainable energy management in low-carbon buildings,” Sustainability, vol. 15, no. 24, pp. 16885, 2023.
A. K. Shaikh, A. Nazir, N. Khalique, A. S. Shah, and N. Adhikari, “A new approach to seasonal energy consumption forecasting using temporal convolutional networks,” Results in Engineering, vol. 19, pp. 101296, 2023.
E. Kesriklioğlu, E. Oktay, and A. Karaaslan, “Predicting total household energy expenditures using ensemble learning methods,” Energy, vol. 276, pp. 127581, 2023.
R. Wazirali, E. Yaghoubi, M. S. S. Abujazar, R. Ahmad, and A. H. Vakili, “State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques,” Electric Power Systems Research, vol. 225, pp. 109792, 2023.
A. M. Priyatno, L. Ningsih, and M. Noor, “Harnessing machine learning for stock price prediction with random forest and simple moving average techniques,” Journal of Engineering and Science Application, vol. 1, no. 1, pp. 1-8, 2024.
H. Eskandari, M. Imani, and M. Parsa Moghaddam, “Best-tree wavelet packet transform bidirectional GRU for short-term load forecasting,” The Journal of Supercomputing, vol. 79, no. 12, pp. 13545-13577, 2023.
S. Kumar and R. Kumar, “Real-time detection of road-based objects using SSD MobileNet-v2 FPNlite with a new benchmark dataset,” in Proceedings of the 4th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), IEEE, pp. 1-5, 2023.
C. Lin, P. Yang, Q. Wang, Z. Qiu, W. Lv, and Z. Wang, “Efficient and accurate compound scaling for convolutional neural networks,” Neural Networks, vol. 167, pp. 787-797, 2023.
S. Sibera, “Electricity Market Dataset [Data set],” Kaggle. DOI: https://doi.org/10.34740/KAGGLE/DSV/10417803, 2025.
X. Chen and S. Güttel, “An efficient aggregation method for the symbolic representation of temporal data,” ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 1, pp. 1-22, 2023.
H. Guo and X. Yan, “Dynamic modeling and simulation of rumor propagation based on the double refutation mechanism,” Information Sciences, vol. 630, pp. 385-402, 2023.
L. Huang, J. Qin, Y. Zhou, F. Zhu, L. Liu, and L. Shao, “Normalization techniques in training DNNs: Methodology, analysis and application,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10173-10196, 2023.
P. Gupta, A. Varshney, M. R. Khan, R. Ahmed, M. Shuaib, and S. Alam, “Unbalanced credit card fraud detection data: A machine learning-oriented comparative study of balancing techniques,” Procedia Computer Science, vol. 218, pp. 2575-2584, 2023.
A. Li, L. Zhang, Y. Liu, and C. Zhu, “Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12514-12524, 2023.
M. M. Taye, “Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions,” Computation, vol. 11, no. 3, pp. 52, 2023.
H. H. Htun, M. Biehl, and N. Petkov, “Survey of feature selection and extraction techniques for stock market prediction,” Financial Innovation, vol. 9, no. 1, pp. 26, 2023.
X. Lei, Y. Xia, A. Wang, X. Jian, H. Zhong, and L. Sun, “Mutual information based anomaly detection of monitoring data with attention mechanism and residual learning,” Mechanical Systems and Signal Processing, vol. 182, pp. 109607, 2023.
D. Kim, A. Khalil, H. Nam, and J. Kwon, “OPEMI: Online performance evaluation metrics index for deep learning-based autonomous vehicles,” IEEE Access, vol. 11, pp. 16951-16963, 2023.
DOI:
https://doi.org/10.31449/inf.v49i24.8032Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
