Е-Публикации
Технически университет - София

Цитирания (Citation/s):
1. Lopez-martin M.,Sanchez-esguevillas A.,Hernandez-callejo L.,Arribas J.I., Carro B., Additive ensemble neural network with constrained weighted quantile loss for probabilistic electric‐load forecasting, Sensors, Open Access, Volume 21, Issue 91 May 2021 Article number 2979, DOI 10.3390/s21092979 - 2021 - в издания, индексирани в Scopus или Web of Science
2. Memarzadeh G.,Keynia F. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm, Electric Power Systems Research, Volume 192March 2021 Article number 106995, DOI 10.1016/j.epsr.2020.106995 - 2021 - в издания, индексирани в Scopus или Web of Science
3. Kuo, P.H. and Huang, C.J., 2018. A high precision artificial neural networks model for short-term energy load forecasting. Energies, 11(1), p.213. - 2018 - в издания, индексирани в Scopus или Web of Science
4. Tian, C., Ma, J., Zhang, C. and Zhan, P., 2018. A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies, 11(12), p.3493. - 2018 - в издания, индексирани в Scopus или Web of Science
5. Buitrago, J. and Asfour, S., 2017. Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs. Energies, 10(1), p.40. - 2017 - в издания, индексирани в Scopus или Web of Science
6. Xie, G., Chen, X. and Weng, Y., 2018. An integrated Gaussian process modeling framework for residential load prediction. IEEE Transactions on Power Systems, 33(6), pp.7238-7248. - 2018 - в издания, индексирани в Scopus или Web of Science
7. Santra, A.S. and Lin, J.L., 2019. Integrating long short-term memory and genetic algorithm for short-term load forecasting. Energies, 12(11), p.2040. - 2019 - в издания, индексирани в Scopus или Web of Science
8. Kim, Seon Hyeog, Gyul Lee, Gu-Young Kwon, Do-In Kim, and Yong-June Shin. "Deep learning based on multi-decomposition for short-term load forecasting." Energies 11, no. 12 (2018): 3433. - 2018 - в издания, индексирани в Scopus или Web of Science
9. Sigauke, C., Nemukula, M.M. and Maposa, D., 2018. Probabilistic hourly load forecasting using additive quantile regression models. Energies, 11(9), p.2208. - 2018 - в издания, индексирани в Scopus или Web of Science
10. Abbas, Farukh, Donghan Feng, Salman Habib, Usama Rahman, Aazim Rasool, and Zheng Yan. "Short term residential load forecasting: An improved optimal nonlinear auto regressive (NARX) method with exponential weight decay function." Electronics 7, no. 12 (2018): 432. - 2018 - в издания, индексирани в Scopus или Web of Science
11. Liu, Da, Kun Sun, Han Huang, and Pingzhou Tang. "Monthly load forecasting based on economic data by decomposition integration theory." Sustainability 10, no. 9 (2018): 3282. - 2018 - в издания, индексирани в Scopus или Web of Science
12. Jung, Seung-Min, Sungwoo Park, Seung-Won Jung, and Eenjun Hwang. "Monthly electric load forecasting using transfer learning for smart cities." Sustainability 12, no. 16 (2020): 6364. - 2020 - в издания, индексирани в Scopus или Web of Science
13. Vaitheeswaran, Sowmya Sevoor, and Varun Raj Ventrapragada. "Wind Power Pattern Prediction in time series measuremnt data for wind energy prediction modelling using LSTM-GA networks." In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-5. IEEE, 2019. - 2019 - в издания, индексирани в Scopus или Web of Science
14. Essallah, S., & Khedher, A. (2019). A comparative study of long-term load forecasting techniques applied to Tunisian grid case. Electrical Engineering, 101(4), 1235-1247. - 2019 - в издания, индексирани в Scopus или Web of Science
15. Jeong, Hee-Myung, and June Ho Park. "Short-term Electric Load Forecasting in Winter and Summer Seasons using a NARX Neural Network." The Transactions of the Korean Institute of Electrical Engineers 66, no. 7 (2017): 1001-1006. - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
16. Farsi, Behnam, Manar Amayri, Nizar Bouguila, and Ursula Eicker. "On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach." IEEE Access 9 (2021): 31191-31212. - 2021 - в издания, индексирани в Scopus или Web of Science
17. Eletter, Shorouq F., Tahira Yasmin, and Ghaleb A. El Refae. "Marketing intelligence in the era of Big Data." TEM Journal 8, no. 3 (2019): 938. - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
18. Xie, Guangrui. "Robust and data-efficient metamodel-based approaches for online analysis of time-dependent systems." PhD diss., Virginia Tech, 2020. - 2020 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
19. Moon, Jaeuk, Seungwon Jung, Sungwoo Park, and Eenjun Hwang. "Conditional tabular GAN-based two-stage data generation scheme for short-term load forecasting." IEEE Access 8 (2020): 205327-205339. - 2020 - в издания, индексирани в Scopus или Web of Science
20. 정희명, 김규한, and 박준호. "오차보정알고리즘을 고려한 방사기저함수 신경망의 일별 최대전력수요예측 적용에 관한 연구." 전기학회논문지 68, no. 2 (2019): 221-227. - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
21. Li, Yuancheng, Rongyan Yang, and Panpan Guo. "Spark-based Parallel OS-ELM Algorithm Application for Short-term Load Forecasting for Massive User Data." Electric Power Components and Systems 48, no. 6-7 (2020): 603-614. - 2020 - в издания, индексирани в Scopus или Web of Science
22. Stratigakos, Akylas, Athanasios Bachoumis, Vasiliki Vita, and Elias Zafiropoulos. "Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks." Energies 14, no. 14 (2021): 4107. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
23. Eletter, Shorouq F., Ghaleb A. El Refae, Abdelhafid K. Belarbic, and Jamal Abu-Rashid. "Predicting energy Ccnsumption using artificial neural networks: a case study of the UAE." Electronic Journal of Applied Statistical Analysis 11, no. 1 (2018): 137-154. - 2018 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
24. Rafi, Shafiul Hasan, Shohana Rahman Deeba, and Eklas Hossain. "A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network." IEEE Access 9 (2021): 32436-32448. - 2021 - в издания, индексирани в Scopus или Web of Science
25. Liu, Xingwei, Shixiong Fan, Jiaqi Qin, Yan Liu, and Wei Wang. "Long-term prediction method of reactive load based on LSTM neural network." In IOP Conference Series: Earth and Environmental Science, vol. 467, no. 1, p. 012041. IOP Publishing, 2020. - 2020 - в издания, индексирани в Scopus или Web of Science
26. Yao, Yuan, Haiyan Xu, Yuqing Chang, Shu Wang, and Guiping Zhou. "Study on Ultra-Short Term Power Load Forecasting Based on Local Similar Days and Long Short-Term Memory Networks." In 2019 Chinese Automation Congress (CAC), pp. 322-327. IEEE, 2019. - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
27. 정희명, and 박준호. "NARX 신경망을 이용한 동· 하계 단기부하예측에 관한 연구." 전기학회논문지 66, no. 7 (2017): 1001-1006. - 2017 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
28. Sharma, Srishti, Rahul Kumar Agrawal, and Madan Mohan Tripathi. "Synergism of Recurrent Neural Network and Fuzzy Logic for Short Term Energy Load Forecasting." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 165-169. IEEE, 2020. - 2020 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
29. Xie, Guangrui, Xi Chen, and Yang Weng. "Enhance load forecastability: Optimize data sampling policy by reinforcing user behaviors." European Journal of Operational Research (2021). - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
30. Chen, Zexi, Delong Zhang, Haoran Jiang, Longze Wang, Yongcong Chen, Yang Xiao, Jinxin Liu, Yan Zhang, and Meicheng Li. "Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”." Journal of Electrical Engineering & Technology (2021): 1-10. - 2021 - в издания, индексирани в Scopus или Web of Science
31. Babushkin, Vladimir, and Gheorghe CĂPĂȚÂNĂ. "Deep learning approach to forecasting electricity price from load data." Studia Universitatis Moldaviae-Științe Exacte și Economice 7 (137) (2021). - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
32. Sigauke, Caston, Daniel Maposa, and Murendeni Maurel Nemukula. "Probabilistic Hourly Loading Forecasting Using AdditiveProbabilistic Hourly Load Forecasting Using Additive Quantile Regression Models." (2018). - 2018 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
33. Chopra, Abhinav Rakesh, and Nirmal Kumar Nair. "Wavelet-Extreme Learning i-Machine for New Zealand Smart Meter Data." In 2021 IEEE Power and Energy Conference at Illinois (PECI), pp. 1-8. IEEE, 2021. - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
34. AYUBA, PETER, BARNA THOMAS LASS, BABANGIDA ZACHARIAH, and PETER AMOS. "STATISTICAL EVALUATION OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK WITH EXOGENOUS INPUTS AND GENETIC ALGORITHM IN FORECASTING ELECTRIC LOAD DEMAND." Kaduna Journal of Postgraduate Research 2, no. 2 (2019). - 2019 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
35. Hirose, Kei, Keigo Wada, Maiya Hori, and Rin-ichiro Taniguchi. "Event Effects Estimation on Electricity Demand Forecasting." Energies 13, no. 21 (2020): 5839. - 2020 - в издания, индексирани в Scopus или Web of Science
36. 諏訪達郎. "日本における海洋空間の利用調整に関する研究." (2021). - 2021 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
37. 25. Lopez-Martin, M., Sanchez-Esguevillas, A., Hernandez-Callejo, L., Arribas, J.I. and Carro, B., 2021. Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting. Sensors, 21(9), p.2979. - 2021 - в издания, индексирани в Scopus или Web of Science

Вид: пленарен доклад в международен форум, публикация в реферирано издание, индексирана в Scopus