1
Faculty of Computers and Information, Mansoura University, Egypt
(hamsahadi213@gmail.com)
2
Faculty of Computers and Information, Mansoura University, Egypt
(azizasasem@mans.edu.eg)
3
Faculty of Computers and Information, Mansoura University, Egypt
(elbakry@mans.edu.eg)
Abstract :
Electrical loading prediction is a key aspect of the power system governing, operating, and scheduling. Energy suppliers can control the running system cost by using a lot of information it provides thereby optimizing the power system operation performance. The demand for the electricity well forcasted means more than half of their energy efficiency. Implementation of this work traces out an in-depth detail of integrated quality time series forecasting models on the prediction of electrical consumption. The primary goal of the study is to assess the performance of two state-of-the-art forecasting models: Deep LSTM version and long short-term memory (LSTM) neural networks, Seasonal autoregressive integrated ma. The main task is to evaluate the models’ precision in predicting daily energy consumption based on the historical demand data, holiday data and other time-related lines of evidence. The performance of the models is assessed based on the Mean Absolute Percentage Error (MAPE). The method covers feature engineering, the data preparation, model selection, and assessment. The generated MAPE values illuminated the performance of the models— SARIMA performed relatively inaccurately, and LSTM and deep LSTM significantly improved, obtaining a very good MAPEs of 7.5% and 7.45%, respectively. Notably, the deep LSTM version shows a superiority in prediction compared to other models, with particular emphasis on capturing the temporal relationships. This study makes a great contribution to the field of energy forecasting as it shows applicability of LSTM- and SARIMA- based models for the very good forecast of the consumption power. It captures the attention on how the LSTM networks at 20% of depth; may help in improving prediction accuracy when there are complex patterns and long-distance dependence is a concern. To utility companies, the grid operators and lawmakers who are out to harness every energy resource, to cut the costs, and ensure a continuous flow of electricity; such results are so very helpful.
Keywords :
Electricity demand forecasting; SARIMA; LSTM; Deep learning; Time series; Energy management
References :
[1] S. Jiao, A. Aue, and H. Ombao, “Functional Time Series Prediction Under Partial Observation of the Future Curve,” J. Am. Stat. Assoc., 2023, doi: 10.1080/01621459.2021.1929248.
[2] W. Freeborough and T. van Zyl, “Investigating Explainability Methods in Recurrent Neural Network Architectures for Financial Time Series Data,” Appl. Sci., 2022, doi: 10.3390/app12031427.
[3] J. Liu, S. Zhang, and J. Wang, “Development and Comparison of Two Computational Intelligence Algorithms for Electrical Load Forecasts with Multiple Time Scales,” 2022. doi: 10.1109/PSGEC54663.2022.9881169.
[4] K. J. A˚ström et al., “Abstracts,” J. Power Sources, 2015.
[5] S. Mahjoub, L. Chrifi-Alaoui, B. Marhic, L. Delahoche, J. B. Masson, and N. Derbel, “Prediction of energy consumption based on LSTM Artificial Neural Network,” 2022. doi: 10.1109/SSD54932.2022.9955883.
[6] T. Y. Kim and S. B. Cho, “Predicting residential energy consumption using CNN-LSTM neural networks,” Energy, 2019, doi: 10.1016/j.energy.2019.05.230.
[7] R. J. Hyndman and G. Athanasopoulos, “Forecasting: Principles and Practice, 2nd edition,” OTexts: Melbourne, Australia., 2019.
[8] S. K. U. Zaman et al., “COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction,” Appl. Sci., 2022, doi: 10.3390/app12073312.
[9] T. Bashir, C. Haoyong, M. F. Tahir, and Z. Liqiang, “Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN,” Energy Reports, 2022, doi: 10.1016/j.egyr.2021.12.067.
[10] D. Naware and A. Mitra, “Weather classification-based load and solar insolation forecasting for residential applications with LSTM neural networks,” Electr. Eng., 2022, doi: 10.1007/s00202-021-01395-2.
[11] G. Aragon, H. Puri, A. Grass, S. Chala, and C. Beecks, “Incremental deep-learning for continuous load prediction in energy management systems,” 2019. doi: 10.1109/PTC.2019.8810793.
[12] X. Guo, Q. Zhao, D. Zheng, Y. Ning, and Y. Gao, “A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price,” Energy Reports, 2020, doi: 10.1016/j.egyr.2020.11.078.
[13] Y. Jin, H. Guo, J. Wang, and A. Song, “A hybrid system based on LSTM for short-term power load forecasting,” Energies, 2020, doi: 10.3390/en13236241.
[14] Y. Zhou, Q. Lin, and D. Xiao, “Application of LSTM-LightGBM Nonlinear Combined Model to Power Load Forecasting,” 2022. doi: 10.1088/1742-6596/2294/1/012035.
[15] H. Hu, X. Xia, Y. Luo, C. Zhang, M. S. Nazir, and T. Peng, “Development and application of an evolutionary deep learning framework of LSTM based on improved grasshopper optimization algorithm for short-term load forecasting,” J. Build. Eng., 2022, doi: 10.1016/j.jobe.2022.104975.
[16] M. F. Alsharekh, S. Habib, D. A. Dewi, W. Albattah, M. Islam, and S. Albahli, “Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM,” Sensors, 2022, doi: 10.3390/s22186913.
[17] ESO Data Portal, “No Title,” Historic Demand Data - Dataset | National Grid Electricity System Operator, 2023, 2023. https://data.nationalgrideso.com/demand/historic-demand-data
[18] S. Kaelble, “The Balancing and Settlement Code for Dummies®,” Elexon specia l Ed., p. 54, 2020.
[19] I. G. Wilson, S. Sharma, J. Day, and N. Godfrey, “Calculating Great Britain’s half-hourly electrical demand from publicly available data,” Energy Strateg. Rev., 2021, doi: 10.1016/j.esr.2021.100743.
[20] G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artif. Intell. Rev., 2020, doi: 10.1007/s10462-020-09838-1.
[21] Y. Hua, Z. Zhao, R. Li, X. Chen, Z. Liu, and H. Zhang, “Deep Learning with Long Short-Term Memory for Time Series Prediction,” IEEE Commun. Mag., 2019, doi: 10.1109/MCOM.2019.1800155.
[22] J. Zhang, P. Wang, R. Yan, and R. X. Gao, “Long short-term memory for machine remaining life prediction,” J. Manuf. Syst., 2018, doi: 10.1016/j.jmsy.2018.05.011.
[23] L. Jing et al., “Gated Orthogonal Recurrent Units: On Learning to Forget,” Neural Comput., 2019, doi: 10.1162/neco_a_01174.
[24] K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Trans. Neural Networks Learn. Syst., 2017, doi: 10.1109/TNNLS.2016.2582924.
[25] C. Yin et al., “SARIMA-Based Medium- and Long-Term Load Forecasting,” Strateg. Plan. Energy Environ., 2023, doi: 10.13052/spee1048-5236.4222.
[26] A. S. Azad et al., “Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study,” Sustain., 2022, doi: 10.3390/su14031843.
[27] W. Zhu et al., “Co-Occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks,” 2016. doi: 10.1609/aaai.v30i1.10451.
| Style | # |
|---|---|
| MLA | Hamsa Hadi Mohammed, Aziza Asem, Hazem El-Bakry. "Advanced Time Series Forecasting Models for Electricity Demand Prediction: A Comparative Study." Fusion: Practice and Applications, Vol. 15, No. 1, 2024 ,PP. 19-31 (Doi : https://doi.org/10.54216/FPA.150102) |
| APA | Hamsa Hadi Mohammed, Aziza Asem, Hazem El-Bakry. (2024). Advanced Time Series Forecasting Models for Electricity Demand Prediction: A Comparative Study. Journal of Fusion: Practice and Applications, 15 ( 1 ), 19-31 (Doi : https://doi.org/10.54216/FPA.150102) |
| Chicago | Hamsa Hadi Mohammed, Aziza Asem, Hazem El-Bakry. "Advanced Time Series Forecasting Models for Electricity Demand Prediction: A Comparative Study." Journal of Fusion: Practice and Applications, 15 no. 1 (2024): 19-31 (Doi : https://doi.org/10.54216/FPA.150102) |
| Harvard | Hamsa Hadi Mohammed, Aziza Asem, Hazem El-Bakry. (2024). Advanced Time Series Forecasting Models for Electricity Demand Prediction: A Comparative Study. Journal of Fusion: Practice and Applications, 15 ( 1 ), 19-31 (Doi : https://doi.org/10.54216/FPA.150102) |
| Vancouver | Hamsa Hadi Mohammed, Aziza Asem, Hazem El-Bakry. Advanced Time Series Forecasting Models for Electricity Demand Prediction: A Comparative Study. Journal of Fusion: Practice and Applications, (2024); 15 ( 1 ): 19-31 (Doi : https://doi.org/10.54216/FPA.150102) |
| IEEE | Hamsa Hadi Mohammed, Aziza Asem, Hazem El-Bakry, Advanced Time Series Forecasting Models for Electricity Demand Prediction: A Comparative Study, Journal of Fusion: Practice and Applications, Vol. 15 , No. 1 , (2024) : 19-31 (Doi : https://doi.org/10.54216/FPA.150102) |