| 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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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
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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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 и/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
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 и/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
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
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 и/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
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
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 и/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 и/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
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
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
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
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
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
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
40. Yue Y., Zheng W., Wu A., Jin X., Huang Z., Zhang H., Ultra-short-term wind speed forecasting based on secondary decomposition and Transformer-MLR combined model, 2025, Electric Power Systems Research, issue 0, vol. 246, DOI 10.1016/j.epsr.2025.111702, issn 03787796 - 2025 - в издания, индексирани в Scopus
41. Kaplan K., Ulkir O., Kuncan F., Optimization and prediction of mechanical properties of TPU-Based wrist hand orthosis using Bayesian and machine learning models, 2025, Measurement: Journal of the International Measurement Confederation, issue 0, vol. 252, DOI 10.1016/j.measurement.2025.117405, issn 02632241 - 2025 - в издания, индексирани в Scopus
42. Wang Y., Chen J., Cao B., Liu X., Zhang X., Energy consumption prediction of cold storage based on LSTM with parameter optimization, 2025, International Journal of Refrigeration, issue 0, vol. 175, pp. 12-24, DOI 10.1016/j.ijrefrig.2025.03.033, issn 01407007 - 2025 - в издания, индексирани в Scopus
43. Peng Z., Yang X., Short- and medium-term power load forecasting model based on a hybrid attention mechanism in the time and frequency domains, 2025, Expert Systems with Applications, issue 0, vol. 278, DOI 10.1016/j.eswa.2025.127329, issn 09574174 - 2025 - в издания, индексирани в Scopus
44. Wu Q., Peng L., Han G., Shu J., Yuan M., Wang B., Deep-learning-based scheduling optimization of wind-hydrogen-energy storage system on energy islands, 2025, Energy, issue 0, vol. 320, DOI 10.1016/j.energy.2025.135107, issn 03605442, eissn 18736785 - 2025 - в издания, индексирани в Scopus
45. Huang J., Guan L., Su Y., Cai Z., Chen L., Li Y., Zhang J., Generator-Level Transient Stability Assessment in Power System Based on Graph Deep Learning with Sparse Hybrid Pooling, 2025, Electronics (Switzerland), issue 6, vol. 14, DOI 10.3390/electronics14061180, eissn 20799292 - 2025 - в издания, индексирани в Scopus
46. Wu Y., Cao Z.L., Liu C., Wang X.F., Temperature field and curing degree prediction of large composite blades based on coupled finite element analysis and machine learning, 2025, Polymer Composites, issue 0, DOI 10.1002/pc.29804, issn 02728397, eissn 15480569 - 2025 - в издания, индексирани в Scopus
47. Li J., Ma Y., Li H., Liu Y., Li Y., A Load Classification Method Based on Hybrid Clustering of Continuous–Discrete Electricity Consumption Characteristics, 2025, Processes, issue 4, vol. 13, DOI 10.3390/pr13041208, eissn 22279717 - 2025 - в издания, индексирани в Scopus
48. Flores-Garrido J.L., Salmeron P., Gomez-Galan J.A., Perez-Valles A., Deep Learning-Based Control of Active Power Filters Using LSTM and GRU Networks for Harmonic and Frequency Estimation, 2025, IEEE Access, issue 0, DOI 10.1109/ACCESS.2025.3564636, eissn 21693536 - 2025 - в издания, индексирани в Scopus
49. Yang Y., Yu C., Power supply quality prediction method based on LSTM and self-attention mechanism, 2025, Journal of Computational Methods in Sciences and Engineering, issue 0, DOI 10.1177/14727978251337953, issn 14727978 - 2025 - в издания, индексирани в Scopus
50. Niu Z., Lv C., Yu Y., Yu B., Zhang L., Fu Z., Cigarette Quality Problem Prediction Based on WOA-BiLSTM-SPA, 2025, Electronics Letters, issue 1, vol. 61, DOI 10.1049/ell2.70292, issn 00135194, eissn 1350911X - 2025 - в издания, индексирани в Scopus
51. Karademir, C., 2025. Derin öğrenme yaklaşımı ile döviz kuru tahmini: Türkiye örneği. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Derin+%C3%B6%C4%9Frenme+yakla%C5%9F%C4%B1m%C4%B1+ile+d%C3%B6viz+kuru+tahmini&btnG= (Google Scholar) - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
52. Saeed, S., 2025. “Intelligent Stress Estimation in Hydraulic Crane Structures Using Deep Sequential Models,” - Master Thesis, https://urn.fi/URN:NBN:fi-fe2025053056428, pp. 1-61 (Google Scholar) - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
53. Flores Garrido, J.L., 2024. “Aportaciones de las redes neuronales al control de filtros activos de potencia ya la identificación de partículas en detectores de radiación nuclear,” (Doctoral dissertation, Universidad de Huelva). Pp. 1-189, https://dialnet.unirioja.es/servlet/dctes?codigo=361790 (Google Scholar) - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
54. Abdurazakov, N.N., Aliev, R., Mirzaalimov, A.A., Kakhkhorov, J.I., Mirzaalimov, N.A., Karimberdiyev, U.B.R. and Muxtorov, S.B., 2024, April. “Feature Engineering for Short Term Residential Load Forecasting Using RNN-Based Neural Networks,” In International conference on Environmental Design, Material Science, and Engineering Technologies (pp. 83-90). Cham: Springer Nature Switzerland. DOI: 10.1007/978-3-031-76025-9_6 (Scopus, Google Scholar) SJR 0.13 - 2024 - в издания, индексирани в Scopus
55. EL Guma F., Analysis of influenza-like illness trends in Saudi Arabia: a comparative study of statistical and deep learning techniques, 2025, Osong Public Health and Research Perspectives, issue 3, vol. 16, pp. 270-284, DOI 10.24171/j.phrp.2025.0080, issn 22109099, eissn 22336052 - 2025 - в издания, индексирани в Scopus
56. Ansari, I., Hassani, K., Malakouti, S.M. and Suratgar, A.A., 2025. “Predicting Electrical Energy Consumption for Finland Using CNN-LSTM Hybrid Model,” Next Research, p.100580. https://doi.org/10.1016/j.nexres.2025.100580 (Google Scholar) - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
57. Durrotunnisa, Faisal M.R., Nugroho R.A., Kartini D., Indriani F., Bachtiar A.M., Classification of Natural Disaster Messages in Social Media using 3D input with 2D/3D CNN BiLSTM, 2025, Icocseti 2025 International Conference on Computer Sciences Engineering and Technology Innovation Proceeding, issue 0, pp. 756-761, DOI 10.1109/ICoCSETI63724.2025.11019044 - 2025 - в издания, индексирани в Scopus
58. Ozupak Y., Mansurov S., Optimizing electricity demand forecasting with a novel RNN-LSTM hybrid model, 2025, Energy Sources Part B Economics Planning and Policy, issue 1, vol. 20, DOI 10.1080/15567249.2025.2531448, issn 15567249, eissn 15567257 - 2025 - в издания, индексирани в Scopus
59. Utama W., Hermana M., Warnana D.D., Lestari W., Zakariah M.N.A., Garini S.A., Indriani R.F., Novian Putra D.P., Abduh M.U.N., Insani A.N.F., Pratama D.S., Noh K.A.M., Latiff A.H.A., Enhancing Magnetotelluric Data Quality Using Deep Learning-Based Denoising Models: A Study of CNN and LSTM, 2025, Journal of Human Earth and Future, issue 2, vol. 6, pp. 399-425, DOI 10.28991/HEF-2025-06-02-010, eissn 27852997 - 2025 - в издания, индексирани в Scopus
60. Kong G., Zhang Q., Liu D., Pan J., Liu K., Predictive Modeling of Osteonecrosis of the Femoral Head Progression Using MobileNetV3_Large and Long Short-Term Memory Network: Novel Approach, 2025, Jmir Medical Informatics, issue 0, vol. 13, DOI 10.2196/66727, eissn 22919694 - 2025 - в издания, индексирани в Scopus
61. Geto A.D., Emiru E.D., Seid N.E., Tessema A.B., Ahmed B.Y., Multimodal based Amharic fake news detection using CNN and attention-based BiLSTM, 2025, Scientific Reports, issue 1, vol. 15, DOI 10.1038/s41598-025-17579-w, eissn 20452322 - 2025 - в издания, индексирани в Scopus
62. Beloev H.I., Saitov S.R., Filimonova A.A., Chichirova N.D., Babikov O.E., Iliev I.K., Short-Term Electrical Load Forecasting Based on XGBoost Model, 2025, Energies, issue 19, vol. 18, DOI 10.3390/en18195144, eissn 19961073 - 2025 - в издания, индексирани в Scopus
63. Basapur A., Metgud L., Tenginakai S., Kalghatgi T., Kulkarni U., Hegde S., Employee Attrition Prediction Using Ensemble Learning, 2026, Lecture Notes in Networks and Systems, issue 0, vol. 1372 LNNS, pp. 431-442, DOI 10.1007/978-981-96-5726-1_30, issn 23673370, eissn 23673389 - 2025 - в издания, индексирани в Scopus
64. Liu T., Li F., Zhang D., Shan L., Zhu H., Du P., Jiang M., Goh H.H., Kurniawan T.A., Huang C., Kong F., Intelligent load forecasting technologies for diverse scenarios in the new power systems, 2026, Renewable and Sustainable Energy Reviews, issue 0, vol. 226, DOI 10.1016/j.rser.2025.116366, issn 13640321, eissn 18790690 - 2025 - в издания, индексирани в Scopus
65. Nandigam S.H., Rao Kudithi N., Smart Energy Prediction for Hybrid Renewable Systems with Zeiler–Fergus Snail Homing Maxout Networks, 2025, Energy Technology, issue 11, vol. 13, DOI 10.1002/ente.202500738, issn 21944288, eissn 21944296 - 2025 - в издания, индексирани в Scopus
66. Wang Y., Huo H., Liu X., Song F., Huang S., Dynamic forecasting of China’s carbon market prices by the coupling of macroeconomic indicators and LSTM model, 2025, Scientific Reports, issue 1, vol. 15, DOI 10.1038/s41598-025-24667-4, eissn 20452322 - 2025 - в издания, индексирани в Scopus
67. El-Arwash H.M., Abdelaziz A.Y., El-Sehiemy R.A., Optimal harmonics prediction for distribution systems powered by multi-energy sources using bidirectional long-short term memory combined with data sequence, 2025, Applied Soft Computing, issue 0, vol. 184, DOI 10.1016/j.asoc.2025.113799, issn 15684946 - 2025 - в издания, индексирани в Scopus
68. Tie R., Li M., Zhou C., Ding N., Research on the application of an improved Autoformer model integrating CNN-attention-BiGRU in short-term power load forecasting, 2025, Evolving Systems, issue 3, vol. 16, DOI 10.1007/s12530-025-09724-7, issn 18686478, eissn 18686486 - 2025 - в издания, индексирани в Scopus
69. Choi Y., Choi Y., Kwon K., Lee J.H., Kong J.S., PERFORMANCE COMPARISON OF VARIOUS TIME-SERIES FORECASTING MODELS FOR BRIDGE SUFFICIENCY RATING PREDICTION, 2025, Journal of Civil Engineering and Management, issue 7, vol. 31, pp. 811-827, DOI 10.3846/jcem.2025.24914, issn 13923730, eissn 18223605 - 2025 - в издания, индексирани в Scopus
70. Lawal Z.K., Yassin H., Ching Lai D.T., Idris A.C., Modeling the complex spatio-temporal dynamics of ocean wave parameters: A hybrid PINN-LSTM approach for accurate wave forecasting, 2025, Measurement Journal of the International Measurement Confederation, issue 0, vol. 252, DOI 10.1016/j.measurement.2025.117383, issn 02632241 - 2025 - в издания, индексирани в Scopus
71. Ismail A.G., Elbanna S.H.A., Mohamed H.S., Prediction of electrical load demand using combined LHS with ANFIS, 2025, Plos One, issue 6, vol. 20, DOI 10.1371/journal.pone.0325747, eissn 19326203 - 2025 - в издания, индексирани в Scopus
72. Zeng Q., Peng F., Han X., Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from multifaceted datasets, 2025, Plos One, issue 4, vol. 20, DOI 10.1371/journal.pone.0317514, eissn 19326203 - 2025 - в издания, индексирани в Scopus
73. Bounaim D., Mouafik S., Li G., Multivariate CNN-Bi-LSTM temporal production forecasting for distributed wind-to-hydrogen integrated systems with efficient data feature extraction, 2026, International Journal of Hydrogen Energy, issue 0, vol. 199, DOI 10.1016/j.ijhydene.2025.152853, issn 03603199 - 2025 - в издания, индексирани в Scopus
74. Lubwama, N.A., Ahakonye, L.A.C., Kim, D.S. and Lee, J.M., 2025. “Optimizing Energy Consumption Patterns with PureChain-Based Federated Learning,” 한국통신학회 학술대회논문집, pp.1670-1671. (Google Scholar) - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
75. Aruna, S.B. and Suchitra, D., 2025. “Data based Reliability Assessment of 11 kV distribution Feeder using A-LSTM and FMEA,” https://doi.org/10.21203/rs.3.rs-8092740/v1, https://www.researchsquare.com/article/rs-8092740/v1 (Google Scholar) - 2025 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
76. Moreira Junior J.R., da Silva R.J., dos Santos Junior C.R., Abreu T., Lopes M.L.M., Analysis and Optimization of Fuzzy ARTMAP Parameters for Multinodal Electric Load Forecasting, 2026, Energies, issue 1, vol. 19, DOI 10.3390/en19010192, eissn 19961073 - 2026 - в издания, индексирани в Scopus
77. Ramasamy S., Anbalagan S., Manikandan D., Ramaswamy K., AI-enhanced operation and control of isolated AC micro-grids with renewable energy and energy storage systems, 2026, Discover Applied Sciences, issue 2, vol. 8, DOI 10.1007/s42452-025-08145-0, eissn 30049261 - 2026 - в издания, индексирани в Scopus
78. Long L., Li L., Zhang L., Fu Q., Zou R., Feng F., Zhang R., PatchConvFormer: A Patch-Based and Convolution-Augmented Transformer for Periodic Metro Energy Consumption Forecasting, 2026, Electronics Switzerland, issue 1, vol. 15, DOI 10.3390/electronics15010178, eissn 20799292 - 2026 - в издания, индексирани в Scopus
79. Hyun D.-H., Ban J., DLinear-BiLSTM hybrid gross load estimation model considering the absence of BTM PV generation data, 2026, Transactions of the Korean Institute of Electrical Engineers, issue 2, vol. 75, pp. 266-275, DOI 10.5370/KIEE.2026.75.2.266, issn 19758359, eissn 22874364 - 2026 - в издания, индексирани в Scopus
80. Jangilwar P.K., Das S., Bhukya J., A CSO-based VAE-LSTM Model in Hourly Solar and Load Forecasting for Enhancing the PV-BES Performance Considering Battery Degradation Cost, 2026, IEEE Transactions on Industry Applications, issue 2, vol. 62, pp. 3083-3093, DOI 10.1109/TIA.2025.3618592, issn 00939994, eissn 19399367 - 2026 - в издания, индексирани в Scopus
81. Wang, YN, Huo, HY, Liu, XC, Song, FF, Huang, SY, Dynamic forecasting of China's carbon market prices by the coupling of macroeconomic indicators and LSTM model, SCIENTIFIC REPORTS, vol 15, 2025, issn: 2045-2322, art_no: ARTN 40735, doi: 10.1038/s41598-025-24667-4, pmid: MEDLINE:41258008 - 2025 - в издания, индексирани в Web of Science
82. Ma, ZG, Vanting, NB, Jorgensen, BN, , Transfer Learning for Residential Electricity Load Forecasting in a Multi -Geographical Context, 2025 IEEE 34TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, ISIE, 2025, issn: 2163-5137, isbn: 979-8-3503-7480-3, eisbn: 979-8-3503-7479-7, doi: 10.1109/151E62713.2025.11124820 - 2025 - в издания, индексирани в Web of Science
83. Alhashemi, K, Altinoz, OT, Power Demand Forecasting in Iraq Using Singular Spectrum Analysis and Kalman Filter-Smoother, IEEE ACCESS, vol 13, 2025, issn: 2169-3536, doi: 10.1109/ACCESS.2025.3611790 - 2025 - в издания, индексирани в Web of Science
84. Muqtadir, A, Li, B, Qi, B, Chen, SS, Shi, K, DualDRNet: A Unified Deep Learning Framework for Customer Baseline Load Estimation and Demand Response Potential Forecasting for Load Aggregators, IEEE ACCESS, vol 13, 2025, issn: 2169-3536, doi: 10.1109/ACCESS.2025.3613248 - 2025 - в издания, индексирани в Web of Science
85. Li, C, A strategy for enhancing system reliability and grid integration using bagging-driven ensemble framework, CHEMICAL PRODUCT AND PROCESS MODELING, 2025, issn: 1934-2659, eissn: 2194-6159, doi: 10.1515/cppm-2025-0099 - 2025 - в издания, индексирани в Web of Science
86. Yang, YQ, Song, K, Luo, HZ, Greenhouse temperature prediction model based on ISGA-AMSCNN-DD, ENVIRONMENTAL TECHNOLOGY, 2025, issn: 0959-3330, eissn: 1479-487X, doi: 10.1080/09593330.2025.2542575, pmid: MEDLINE:40789322 - 2025 - в издания, индексирани в Web of Science
87. Tang, YC, Huang, XX, Chen, XL, Feng, ZX, Peng, ZT, Gan, M, Ji, ZY, Sun, ZQ, Fan, XH, Sintering yield prediction model based on time series feature extraction and composite neural network, IRONMAKING & STEELMAKING, 2025, issn: 0301-9233, eissn: 1743-2812, doi: 10.1177/03019233251357435 - 2025 - в издания, индексирани в Web of Science
88. Kollu, PK, Babu, AHS, Anil, P, Reddy, VV, Unveiling patterns in top mobile applications using LSTM and mBERT, NEW REVIEW OF HYPERMEDIA AND MULTIMEDIA, vol 31, 2025, issn: 1361-4568, eissn: 1740-7842, doi: 10.1080/13614568.2025.2504348 - 2025 - в издания, индексирани в Web of Science
89. Xiong, B, Li, JS, Li, M, Jin, Z, Luo, X, Li, TT, Yao, ZL, Medium-long-term prediction of NPP based on an improved spatio-temporal attention mechanism for convolutional long short-term memory network, INTERNATIONAL JOURNAL OF REMOTE SENSING, vol 46, 2025, issn: 0143-1161, eissn: 1366-5901, doi: 10.1080/01431161.2025.2518506 - 2025 - в издания, индексирани в Web of Science
90. Duy, HA, Srisongkram, T, Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks with Conjoint Fingerprints: Application in Predicting Skin-Sensitizing Agents in Natural Compounds, JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol 65, 2025, issn: 1549-9596, eissn: 1549-960X, doi: 10.1021/acs.jcim.5c00032, pmid: MEDLINE:40029998 - 2025 - в издания, индексирани в Web of Science
Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science
