| Autors: Pavlatos, C., Makris, E., Fotis, G., Vita, V., Mladenov, V. M. Title: Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network Keywords: forecastingelectricitydemand;bidirectionalLSTM;short-termpre Abstract: Preciseanticipationofelectricaldemandholdscrucialimportancefortheoptimaloperation ofpowersystemsandtheeffectivemanagementofenergymarketswithinthedomainofenergy planning. Thisstudybuildsonpreviousresearchfocusedontheapplicationofartificialneural networkstoachieveaccurateelectricalloadforecasting. Inthispaper,animprovedmethodology isintroduced,centeringaroundbidirectionalLongShort-TermMemory(LSTM)neuralnetworks (NN).TheprimaryaimoftheproposedbidirectionalLSTMnetworkistoenhancepredictiveperformancebycapturingintricatetemporalpatternsandinterdependencieswithintimeseriesdata. Whileconventionalfeed-forwardneuralnetworksaresuitableforstandalonedatapoints,energy consumptiondataarecharacterizedbysequentialdependencies,necessitatingtheincorporation ofmemory-basedconcepts. ThebidirectionalLSTMmodel isdesignedtofurnishtheprediction frameworkwiththecapacitytoassimilateandleverageinformationfrombothprecedingandforthcomingtimesteps.Thisaugmentationsignificantlybolsterspredictivecapabilitiesbyencapsu References Issue
|
Цитирания (Citation/s):
1. Fotis, G., Sijakovic, N., Zarkovic, M., Ristic, V., Terzic, A., Vita, V., Zafeiropoulou, M., Zoulias, E. and Maris, T.I., 2023. „Forecasting Wind and Solar Energy Production in the Greek Power System using ANN Models,“ WSEAS Transactions on Power Systems, vol. 18, pp. 373-391. ISSN 17905060, DOI 10.37394/232016.2023.18.38 (Scopus, Google Scholar) SJR 0.162. - 2023 - в издания, индексирани в Scopus или Web of Science
2. Nasab, M.A., Alizadeh, M., Nasimov, R., Zand, M., Nasab, M.A. and Padmanaban, S., 2024. “Planning with the electricity market One day ahead for a smart home connected to the RES by the MILP method,” Renewable Energy Focus, vol. 50, ISSN 17550084, DOI 10.1016/j.ref.2024.100606 pp. 1-17, (Web of Science, Scopus, Google Scholar) SJR 0.944, IF 4.4 - 2024 - в издания, индексирани в Scopus или Web of Science
3. Dong, Z., Kong, J., Yan, W., Wang, X. and Li, H., 2024. “Multivariable High-Dimension Time-Series Prediction in SIoT via Adaptive Dual-Graph-Attention Encoder-Decoder With Global Bayesian Optimization,” IEEE Internet of Things Journal. ISSN 23274662, DOI 10.1109/JIOT.2024.3418993, pp. 1-1 (Web of Science, Scopus, Google Scholar) SJR 3.382 - 2024 - в издания, индексирани в Scopus или Web of Science
4. Karthick, A., Shankar, R. and Dharmaraj, G., 2024. “Energy forecasting of the building integrated photovoltaic system based on deep learning Dragonfly-firefly algorithm,” Energy, pp. 1-8, ISSN 03605442, DOI 10.1016/j.energy.2024.132926 (Web of Science, Scopus, Google Scholar) IF 9.0, SJR 2.11 - 2024 - в издания, индексирани в Scopus или Web of Science
5. Li, F., Liu, S., Wang, T. and Liu, R., 2024. “Optimal planning for integrated electricity and heat systems using CNN-BiLSTM-attention network forecasts,” Energy, vol. 309, pp. 1-13, ISSN 03605442, DOI 10.1016/j.energy.2024.133042 (Web of Science, Scopus, Google Scholar) IF 9.0, SJR 2.11 - 2024 - в издания, индексирани в Scopus или Web of Science
6. Debnath, K.B., Jenkins, D.P., Patidar, S. and Peacock, A.D., 2024. „Remote work might unlock solar PV's potential of cracking the ‘Duck Curve’“ Applied Energy, vol. 367, pp. 1-17, ISSN 03062619, DOI 10.1016/j.apenergy.2024.123378 (Web of Science, Scopus, Google Scholar) IF 10.1, SJR 2.82 - 2024 - в издания, индексирани в Scopus или Web of Science
7. Jin, X., Wang, S., Hu, Q., Zhang, Y., Qiu, P., Liu, Y. and Dou, X., 2024. „Estimating air conditioning energy consumption of residential buildings using hourly smart meter data,“ Journal of Building Engineering, vol. 97, pp. 1-18, ISSN 23527102, DOI 10.1016/j.jobe.2024.110729 (Web of Science, Scopus, Google Scholar) SJR 1.397, IF 6.7 - 2024 - в издания, индексирани в Scopus или Web of Science
8. Kilinc, H.C., Apak, S., Ozkan, F., Ergin, M.E. and Yurtsever, A., 2024. „Multimodal Fusion of Optimized GRU–LSTM with Self-Attention Layer for Hydrological Time Series Forecasting,“ Water Resources Management, pp.1-18. ISSN 09204741, DOI 10.1007/s11269-024-03943-4 (Web of Science, Scopus, Google Scholar) IF 4.1, SJR 0.898 - 2024 - в издания, индексирани в Scopus или Web of Science
9. Wan, S., Wang, Y., Zhang, Y., Zhu, B., Huang, H. and Liu, J., 2024. „Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction,“ Sustainability, vol. 16, issue (16), pp. 1-23, ISSN 20711050, DOI 10.3390/su16166903 (Web of Science, Scopus, Google Scholar) IF 3.6, SJR 0.672 - 2024 - в издания, индексирани в Scopus или Web of Science
10. Alshahrani, R., Rizwan, A., Alomar, M.A. and Fotis, G., 2024. „IoT-Based Sustainable Energy Solutions for Small and Medium Enterprises (SMEs),“ Energies, vol. 17, issue (16), pp. 1-21, https://doi.org/10.3390/en17164144 (Web of Science, Scopus, Google Scholar) IF 3.0, SJR 0.651 - 2024 - в издания, индексирани в Scopus или Web of Science
11. Alkhawaji, R.N., Serbaya, S.H., Zahran, S., Vita, V., Pappas, S., Rizwan, A. and Fotis, G., 2024. „Enhanced Coconut Yield Prediction Using Internet of Things and Deep Learning: A Bi-Directional Long Short-Term Memory Lévy Flight and Seagull Optimization Algorithm Approach,“ Applied Sciences, vol. 14, issue (17), pp. 1-20, ISSN 20763417, DOI 10.3390/app14177516 (Web of Science, Scopus, Google Scholar) SJR 0.508, IF 2.5 - 2024 - в издания, индексирани в Scopus или Web of Science
12. Chen, J., Xie, Y., Chen, K., Zhen, M. and Hu, X., 2025. „QRNet: Query-based reparameterization net for real-time detection of power adapter surface defects,” Measurement, vol. 239, ISSN 02632241, DOI 10.1016/j.measurement.2024.115420, pp. 1-12, (Web of Science, Scopus, Google Scholar) SJR 1.181, IF 5.2 - 2024 - в издания, индексирани в Scopus или Web of Science
13. Jin, Y., Li, Y., He, B., Yang, X. and Zheng, L., 2024. “Mass estimation of tractor-semitrailer systems: An approach of dynamics and data fusion-driven in real environments,” Measurement, vol. 238, pp. 1-18, ISSN 02632241, DOI 10.1016/j.measurement.2024.115367 (Web of Science, Scopus, Google Scholar) IF 5.2, SJR 1.181 - 2024 - в издания, индексирани в Scopus или Web of Science
14. Goyal, H.R., Almusawi, M., Otero-Potosi, S., Varshney, N., Sharma, V. and Rao, A.K., 2024, May. “Predictive Maintenance in Smart Grids with Long Short-Term Memory Networks (LSTM),” In 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) (pp. 1370-1375). IEEE. (Scopus, Google Scholar) - 2024 - в издания, индексирани в Scopus или Web of Science
15. Birdal, R.G., 2024. “Air pollution impact on forecasting electricity demand utilizing CNN-PSO hyper-parameter optimization,” Environmental Research Communications, vol. 6, issue (5), ISSN 25157620, DOI 10.1088/2515-7620/ad484b pp. 1-12, (Web of Science, Scopus, Google Scholar) IF 3.0, SJR 0.78 - 2024 - в издания, индексирани в Scopus или Web of Science
16. Palma, G., Chengalipunath, E.S.J. and Rizzo, A., 2024. “Time Series Forecasting for Energy Management: Neural Circuit Policies (NCPs) vs. Long Short-Term Memory (LSTM) Networks,” Electronics, vol. 13, issue (18), pp. 1-27, https://doi.org/10.3390/electronics13183641 (Web of Science, Scopus, Google Scholar) IF 2.6, SJR 0.644 - 2024 - в издания, индексирани в Scopus или Web of Science
17. Liu, J., Wang, Y., Zhou, Y., Shang, C. and Huang, D., 2024. “Fast Trend Extraction of Industrial Process Data Based on Deep Bidirectional LSTM,” IFAC-PapersOnLine, vol. 58, issue (4), ISSN 24058963, DOI 10.1016/j.ifacol.2024.07.265, pp.484-489. (Scopus, Google Scholar) SJR 0.365 - 2024 - в издания, индексирани в Scopus или Web of Science
18. Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V. and Borodulin, A., 2024. “Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review,” Polymers, vol. 16, issue (18), pp. 1-44, (Web of Science, Scopus, Google Scholar) SJR 0.8, IF 4.7 - 2024 - в издания, индексирани в Scopus или Web of Science
19. Gong, R., Wei, Z., Qin, Y., Liu, T. and Xu, J., 2024. “Short-Term Electrical Load Forecasting Based on IDBO-PTCN-GRU Model,” Energies, vol. 17, issue (18), pp. 1-24, ISSN 19961073, DOI 10.3390/en17184667 (Web of Science, Scopus, Google Scholar) IF 3.0, SJR 0.651 - 2024 - в издания, индексирани в Scopus или Web of Science
20. Kamalov, F., Zicmane, I., Safaraliev, M., Smail, L., Senyuk, M. and Matrenin, P., 2024. “Attention-Based Load Forecasting with Bidirectional Finetuning,” Energies, vol. 17, issue (18), pp. 1-16, ISSN 19961073, DOI 10.3390/en17184699 (Web of Science, Scopus, Google Scholar) IF 3.0, SJR 0.651 - 2024 - в издания, индексирани в Scopus или Web of Science
21. Fotis, G., 2024. “An improved arithmetic method for determining the optimum placement and size of EV charging stations,” Computers and Electrical Engineering, vol. 120, pp. 1-16, ISSN 00457906, DOI 10.1016/j.compeleceng.2024.109840 (Scopus, Google Scholar) SJR 1.041, IF 4.0 - 2024 - в издания, индексирани в Scopus или Web of Science
22. Baniya, B. and Giurco, D., 2024. “Cost-effective and optimal pathways to selecting building microgrid components–The resilient, reliable, and flexible energy system under changing climate conditions,” Energy and Buildings, pp. 1-16, ISSN 03787788, DOI 10.1016/j.enbuild.2024.114896 (Web of Science, Scopus, Google Scholar) IF 6.7, SJR 1.632 - 2024 - в издания, индексирани в Scopus или Web of Science
23. Akter, R., Shirkoohi, M. G., Wang, J., & Mérida, W. (2024). “An efficient hybrid deep neural network for multi-horizon forecasting of academic building power loads,” Energy and Buildings, 115217. ISSN 03787788, DOI 10.1016/j.enbuild.2024.115217, pp. 1-19 (Scopus, Google Scholar) SJR 1.632, IF 6.6 - 2024 - в издания, индексирани в Scopus или Web of Science
24. Zhang, B., Yin, Y., Li, B., He, S. and Song, J., 2025. “A hybrid algorithm for predicting the remaining service life of hybrid bearings based on bidirectional feature extraction,” Measurement, 242, p.116152. ISSN 02632241, DOI 10.1016/j.measurement.2024.116152, pp. 1-15 (Web of Science, Scopus, Google Scholar) IF 5.2, SJR 1.181 - 2025 - в издания, индексирани в Scopus или Web of Science
25. Zabin, R., Haque, K.F. and Abdelgawad, A., 2024. “PredXGBR: A Machine Learning Framework for Short-Term Electrical Load Prediction,” Electronics, vol. 13, issue (22), pp. 1-26, ISSN 20799292, DOI 10.3390/electronics13224521 (Web of Science, Scopus, Google Scholar) IF 2.6, SJR 0.644 - 2024 - в издания, индексирани в Scopus или Web of Science
26. Zare, M.S., Nikoo, M.R., Chen, M. and Gandomi, A.H., 2025. “Capturing complex electricity load patterns: A hybrid deep learning approach with proposed external-convolution attention,” Energy Strategy Reviews, vol. 57, pp. 1-15, ISSN 2211467X, DOI 10.1016/j.esr.2025.101638 (Scopus, Google Scholar) IF 8.0, SJR 1.899 - 2025 - в издания, индексирани в Scopus или Web of Science
27. Bhatnagar, M., Rozinaj, G. and Vargic, R., 2025. “Using Crafted Features and Polar Bear Optimization Algorithm for Short-Term Electric Load Forecast System,” Energy and AI, pp. 1-15, ISSN 26665468, DOI 10.1016/j.egyai.2025.100470 (Scopus, Google Scholar) IF 9.6, SJR 2.16 - 2025 - в издания, индексирани в Scopus или Web of Science
28. Tang, B., Hu, J., Yang, M., Zhang, C. and Bai, Q., 2024. “Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models,” Applied Sciences, vol. 14, issue (24), pp. 1-20, ISSN 20763417, DOI 10.3390/app142411606 (Web of Science, Scopus, Google Scholar) IF 2.7, SJR 0.508 - 2024 - в издания, индексирани в Scopus или Web of Science
29. Li, J., Lv, Y., Zhou, Z., Du, Z., Wei, Q. and Xu, K., 2025. “Identification and Correction of Abnormal, Incomplete Power Load Data in Electricity Spot Market Databases,” Energies, vol. 18, issue (1), pp. 1-14, ISSN 19961073, DOI 10.3390/en18010176 (Web of Science, Scopus, Google Scholar) IF 3.0, SJR 0.651 - 2025 - в издания, индексирани в Scopus или Web of Science
30. Heng, S., 2025. “Enhanced multi-energy load forecasting via multi-task learning and GRU-attention networks in integrated energy systems,” Electrical Engineering, pp.1-11. ISSN 09487921, DOI 10.1007/s00202-024-02942-3 (Web of Science, Scopus, Google Scholar) IF 1.7, SJR 0.431 - 2025 - в издания, индексирани в Scopus или Web of Science
31. You, B., Xing, C., Yu, Q., Zhang, M., He, Z., Zeng, L. and Liu, C., 2024, October. “Federated Learning with LSTM Neural Network for Regional Load Forecasting,” In 2024 21st International Conference on Harmonics and Quality of Power (ICHQP) (pp. 619-624). IEEE. ISSN 15406008, ISBN 979-835038256-3, DOI 10.1109/ICHQP61174.2024.10768807 (Scopus, Google Scholar) SJR 0.333 - 2024 - в издания, индексирани в Scopus или Web of Science
32. De Jesus, N.M., Festijo, E.D., Apolinario, G.F.D. and Lopez, D.J.D., 2024, June. “Hybrid BiLSTM-PSO Approach for Multi-Metering Point Day-Ahead Electrical Load Forecasting,” In 2024 8th International Conference on Power Energy Systems and Applications (ICoPESA) (pp. 326-332). IEEE. ISBN 979-835035166-8, DOI 10.1109/ICOPESA61191.2024.10743580 (Scopus, Google Scholar) - 2024 - в издания, индексирани в Scopus или Web of Science
33. Li, D., Qi, Z., Zhou, Y. and Elchalakani, M., 2025. “Machine Learning Applications in Building Energy Systems: Review and Prospects,” Buildings, vol. 15, issue (4), pp. 1-26, https://doi.org/10.3390/buildings15040648 (Web of Science, Scopus, Google Scholar) IF 3.1, SJR 0.575 - 2025 - в издания, индексирани в Scopus или Web of Science
34. Akter, R., Shirkoohi, M.G., Wang, J. and Mérida, W., 2025. “An efficient hybrid deep neural network model for multi-horizon forecasting of power loads in academic buildings,” Energy and Buildings, vol. 329, pp. 1-19, ISSN 03787788, DOI 10.1016/j.enbuild.2024.115217 (Web of Science, Scopus, Google Scholar) IF 6.7, SJR 1.632 - 2025 - в издания, индексирани в Scopus или Web of Science
35. Chen, Z., Chen, J., Zhu, Z., Chen, J., Lv, T., Qiao, D. and Zheng, Y., 2025. “Cloud-based estimation of lithium-ion battery life for electric vehicles using equivalent circuit model and recurrent neural network,” Journal of Energy Storage, vol. 114, pp. 1-12, ISSN 2352152X, DOI 10.1016/j.est.2025.115718 (Scopus, Google Scholar) IF 8.9, SJR 1.595 - 2025 - в издания, индексирани в Scopus или Web of Science
36. Ali, M., Li, B., Ying, Z., Songsong, C. and Kazmi, S.N., 2025. “Day-Ahead Demand Response Potential Prediction in Residential Buildings with HITSKAN: A Fusion of Kolmogorov-Arnold Networks and N-HiTS,” Energy and Buildings, pp. 1-14, vol. 332, ISSN 03787788, DOI 10.1016/j.enbuild.2025.115455 (Scopus, Google Scholar) IF 6.6, SJR 1.632 - 2025 - в издания, индексирани в Scopus или Web of Science
37. Li, K., Zhou, S., Zhao, M. and Wei, B., 2025. “Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy,” Energies, vol. 18, issue (3), pp. 1-22, ISSN 19961073, DOI 10.3390/en18030686 (Web of Science, Scopus, Google Scholar) SJR 0.651, IF 3.0 - 2025 - в издания, индексирани в Scopus или Web of Science
38. Cao, W., Liu, H., Zhang, X., Zeng, Y. and Ling, X., 2025. “Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors,” Sustainability, vol. 17, issue (3), pp. 1-21, ISSN 20711050, DOI 10.3390/su17031033 (Web of Science, Scopus, Google Scholar) SJR 0.672, IF 3.6 - 2025 - в издания, индексирани в Scopus или Web of Science
39. Boujamza, A. and Lissane Elhaq, S., 2025. “Predicting Oil Temperature in Electrical Transformers Using Neural Hierarchical Interpolation,” Journal of Engineering, vol. 2025, issue (1), pp. 1-11, ISSN:2314-4904, https://doi.org/10.1155/je/9714104 (Web of Science, Google Scholar) IF 2.0 - 2025 - в издания, индексирани в Scopus или Web of Science
Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science
