The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation
Open accessAbstract
Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This algorithm has proven successful in several diagnostic applications (Behravan et al., 2018; Torlay et al., 2017; Zheng et al., 2017).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Zheng et al., 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
(1967) · Cited by 23,719
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Learning to Forget: Continual Prediction with LSTM
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000) · Neural Computation · doi:10.1162/089976600300015015 · Cited by 6,122
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369
On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310
Supervised Sequence Labelling with Recurrent Neural Networks
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
Flagged first, then by citations
Research works printed but not linked: 2
- Mitchell, T.M. (1997). Machine Learning, McGraw Hill.
- Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.
Cited by
Sorted by citations
Items 301–400 of 638
Boosted Multi-Task Learning for Inter-District Collaborative Load Forecasting
Liu, H., Zhang, X., Sun, H., & Shahidehpour, M. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3266342 · Cited by 19
ISLAM, M. B., Hosseini, P., Kakhani, A., Jalayer, M., & Patel, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-73134-z · Cited by 17
Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis
Hussain, F., Hasanuzzaman, M., & Rahim, N. A. (2025) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2025.100903 · Cited by 17
Yan, X., Zhang, L., Li, J., Du, D., & Hou, F. (2020) · Entropy · doi:10.3390/e22020241 · Cited by 17
Deep multi-task learning model for time series prediction in wireless communication
Cao, K., Hu, T., Li, Z., Zhao, G., & Qian, X. (2020) · Physical Communication · doi:10.1016/j.phycom.2020.101251 · Cited by 17
Fan, S., & Liu, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9491748 · Cited by 17
Arslan, E., & Uyulan, Ç. (2022) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070221100111 · Cited by 16
A new hybrid optimization prediction strategy based on SH-Informer for district heating system
Zhao, Y., Gong, M., Sun, J., Han, C., Jing, L., Li, B., & Zhao, Z. (2023) · Energy · doi:10.1016/j.energy.2023.129010 · Cited by 16
Gao, W., Zang, M., & Mei, G. (2023) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-023-03466-z · Cited by 16
Optimizing Bi-LSTM networks for improved lung cancer detection accuracy
Diao, S., Wan, Y., Huang, D., Huang, S., Sadiq, T., Khan, M. S., Hussain, L., Alkahtani, B. S. T., & Mazhar, T. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316136 · Cited by 18
Zhou, K., Zhang, Z., Liu, J., Hu, Z., Duan, X., & Xu, Q. (2018) · Measurement Science and Technology · doi:10.1088/1361-6501/aaee5e · Cited by 16
Improving protein fold recognition using triplet network and ensemble deep learning
Liu, Y., Han, K., Zhu, Y., Zhang, Y., Shen, L.-C., Song, J., & Yu, D. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab248 · Cited by 16
Feng, S., Karanth, S., Almuhaideb, E., Parveen, S., & Pradhan, A. K. (2024) · Food Research International · doi:10.1016/j.foodres.2024.114464 · Cited by 15
Tian, Z., Chen, D., & Zhao, L. (2024) · Applied Sciences · doi:10.3390/app14052137 · Cited by 15
Power Load Forecast Based on CS-LSTM Neural Network
Han, L., Wang, X., Yu, Y., & Wang, D. (2024) · Mathematics · doi:10.3390/math12091402 · Cited by 15
A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
Shiwakoti, R. K., Charoenlarpnopparut, C., & Chapagain, K. (2024) · Applied Sciences · doi:10.3390/app14103971 · Cited by 18
Rahaman, M. M., Islam, M. R., Manik, M. M. T. G., Aziz, M. A., Noman, I. R., Bhuiyan, M. M. R., Bishnu, K. K., & Bortty, J. C. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16080432 · Cited by 15
Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models
Dong, J., Wu, L., Liu, X., Fan, C., Leng, M., & Yang, Q. (2020) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2020.09014 · Cited by 15
Hybrid Machine Learning Classifier and Ensemble Techniques to Detect Parkinson’s Disease Patients
Yadav, S. K., & Singh, M. K. (2021) · SN Computer Science · doi:10.1007/s42979-021-00587-8 · Cited by 15
Moguilner, S., Birba, A., Fino, D., Isoardi, R., Huetagoyena, C., Otoya, R. E., Tirapu, V., Cremaschi, F., Sedeño, L., Ibáñez, A. M., et al. (2021) · Cortex · doi:10.1016/j.cortex.2021.08.003 · Cited by 17
Short-term load forecasting based on CEEMDAN-FE-ISSA-LightGBM model
Li, Z., & Chen, Z. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1111786 · Cited by 15
An optimized time series combined forecasting method based on neural networks
Zhao, K., Li, L., Cai, S., & Sun, R. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-03064-5 · Cited by 15
An Efficient CNN and KNN Data Analytics for Electricity Load Forecasting in the Smart Grid
Aimal, S., Javaid, N., Islam, T., Khan, W. Z., Aalsalem, M. Y., & Sajjad, H. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-15035-8_57 · Cited by 15
Gao, P., Yang, Y., Fei, L., Ge, J., Yin, Q., & Wang, R. (2024) · Energy · doi:10.1016/j.energy.2024.133390 · Cited by 14
Petroșanu, D.-M. (2019) · Processes · doi:10.3390/pr7050310 · Cited by 15
Meta-learning in multivariate load demand forecasting with exogenous meta-features
Arjmand, A., Samizadeh, R., & Saryazdi, M. D. (2020) · Energy Efficiency · doi:10.1007/s12053-020-09851-x · Cited by 14
Peng, X., Cheng, K., Lang, J., Zhang, Z., Cai, T., & Duan, S. (2021) · Energies · doi:10.3390/en14071894 · Cited by 14
Machine learning based novel ensemble learning framework for electricity operational forecasting
Weeraddana, D., Khoa, N. L. D., & Mahdavi, N. (2021) · Electric Power Systems Research · doi:10.1016/j.epsr.2021.107477 · Cited by 14
Zhao, C., Zhao, X., Zhao, L., & Zhang, Q. (2022) · Sustainability · doi:10.3390/su14116829 · Cited by 14
Evaluating Forecasting Techniques for Integrating Household Energy Prosumers into Smart Grids
Petrican, T., Vesa, A. V., Antal, M., Pop, C. D., Cioara, T., Anghel, I. M., & Salomie, I. (2018) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2018.8516617 · Cited by 15
Efficient Strategies of Static Features Incorporation into the Recurrent Neural Network
Miebs, G., Mochol-Grzelak, M., Karaszewski, A., & Bachorz, R. A. (2020) · Neural Processing Letters · doi:10.1007/s11063-020-10195-x · Cited by 16
Void fraction measurement using modal decomposition and ensemble learning in vertical annular flow
Li, C., Liu, M., Zhao, N., Wang, F., Zhao, Z., Guo, S., Fang, L., & Li, X. (2021) · Chemical Engineering Science · doi:10.1016/j.ces.2021.116929 · Cited by 14
Seyrek, E. C., Narin, Ö. G., & Uysal, M. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01747-7 · Cited by 16
Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review
Ahmad, F. A., Liu, J., Hashim, F., & Samsudin, K. B. (2024) · International Journal of Technology · doi:10.14716/ijtech.v15i1.5543 · Cited by 13
Essential elements of physical fitness analysis in male adolescent athletes using machine learning
Lee, Y.-H., Chang, J., Lee, J.-E., Jung, Y., Lee, D., & Lee, H.-S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298870 · Cited by 13
Han, D., Guo, W., Chen, H., Wang, B., & Guo, Z. (2024) · International Review of Economics & Finance · doi:10.1016/j.iref.2024.103508 · Cited by 13
Zhangding, L., Wang, X., Li, Q., Fei, W., & Tao, X. (2025) · Wearable electronics. · doi:10.1016/j.wees.2024.12.005 · Cited by 13
Mandal, A., & Rajput, S. P. S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10233-8 · Cited by 13
Sampaio, R. J., Rodríguez, D. A., Espíndola, R. P., & da Silva, F. P. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106365 · Cited by 14
Time and feature varying tourism demand forecasting
Gao, H., Li, H., & Zhang, C. (2025) · Annals of Tourism Research · doi:10.1016/j.annals.2025.103959 · Cited by 13
Patil, R., Singh, S., & Agarwal, S. (2020) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2020.semeval-1.226 · Cited by 14
Liu, X., Fan, S., Cao, F., Peng, S., & Huang, H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8340371 · Cited by 13
RetractedShort-term load forecasting for multiple buildings: A length sensitivity-based approach
Chen, Y., & Chen, Z. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.10.425 · Cited by 13
Zhu, J., Yang, J., Peng, M., Liang, X., & Ben, C. (2025) · Energy · doi:10.1016/j.energy.2025.139195 · Cited by 13
Maged, A., Haridy, S., Hosny, M., & Shen, H. (2026) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2026.2613018 · Cited by 12
Tetik, B., Doğan, G. M., Paşahan, R., Durak, M. A., Güldoğan, E., Saraç, K., Önal, Ç., & Yıldırım, İ. O. (2021) · International Journal of Clinical Practice · doi:10.1111/ijcp.14746 · Cited by 12
Regression model-based hourly aggregated electricity demand prediction
Panigrahi, R., Patne, N. R., Pemmada, S., & Manchalwar, A. D. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.10.004 · Cited by 12
Time–frequency analysis of ultrasonic signals for quality assessment of bonded concrete
Khademi, P., Mousavi, M., Dackermann, U., & Gandomi, A. H. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.133062 · Cited by 12
Kuizinienė, D., Savickas, P., Kunickaitė, R., Juozaitienė, R., Damaševičius, R., Maskeliūnas, R., & Krilavičius, T. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1956 · Cited by 12
Hybrid deep learning models with data fusion approach for electricity load forecasting
Özen, S., Yazıcı, A., & Atalay, V. (2024) · Expert Systems · doi:10.1111/exsy.13741 · Cited by 12
A simulation-optimization approach for supporting conservative water allocation under uncertainties
Cai, Y., Li, T., Zhang, Y., & Zhang, X. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.115073 · Cited by 12
Lin, S., & Chen, N. (2022) · Geomorphology · doi:10.1016/j.geomorph.2022.108405 · Cited by 12
Mohsenimanesh, A. A., Entchev, E., & Bosnjak, F. (2022) · Applied Sciences · doi:10.3390/app12189288 · Cited by 12
Short-term load forecasting using time series clustering
Martins, A., Lagarto, J., Canacsinh, H., Reis, F., & Cardoso, M. G. M. S. (2022) · Optimization and Engineering · doi:10.1007/s11081-022-09760-1 · Cited by 12
Enhanced Automated Deep Learning Application for Short-Term Load Forecasting
Laitsos, V. M., Vontzos, G., Bargiotas, D., Daskalopulu, A., & Tsoukalas, L. H. (2023) · Mathematics · doi:10.3390/math11132912 · Cited by 12
Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid
Son, H.-G., Kim, Y., & Kim, S. (2020) · Energies · doi:10.3390/en13092377 · Cited by 11
A Novel Hybrid Model Combining BPNN Neural Network and Ensemble Empirical Mode Decomposition
Li, H., Wang, Q., & Wei, D. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00446-3 · Cited by 11
Ibrahim, A.-W., Farh, H. M. H., & Al-Shamma’a, A. A. (2025) · Mathematics · doi:10.3390/math13172900 · Cited by 11
Multi-WRNN model for pricing the crude oil futures market
Hajiabotorabi, Z., Samavati, F. F., Ghaini, F. M. M., & Shahmoradi, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115229 · Cited by 11
Reactive Power Load Forecasting based on K-means Clustering and Random Forest Algorithm
Cheng, X., Wang, T., Li, Y., Pi, J., Li, M., & Liu, B. (2021) · International Conference on Power System Technology (POWERCON) · doi:10.1109/powercon53785.2021.9697840 · Cited by 11
Fujita, K., Hiyama, T., Wada, K., Aihara, T., Matsumura, Y., Hamatsuka, T., Yoshinaka, Y., Kimura, M., & Kuzuya, M. (2022) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2022.104793 · Cited by 11
Click prediction boosting via Bayesian hyperparameter optimization-based ensemble learning pipelines
Demirel, Ç., Tokuç, A. A., & Tekín, A. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200185 · Cited by 11
Prediction of Health Insurance Claims Using Logistic Regression and XGBoost Methods
Permai, S. D., & Herdianto, K. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.610 · Cited by 11
An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting
Yin, C., Wei, N., Wu, J., Ruan, C., Luo, X., & Zeng, F. B. (2024) · Energies · doi:10.3390/en17020307 · Cited by 11
GRU-based Encoder-Decoder for Short-term CHP Heat Load Forecast
Lu, K., Meng, X. R., Sun, W. X., Zhang, R. G., Han, Y. K., Gao, S., & Su, D. (2018) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/392/6/062173 · Cited by 11
Ye, Z., O’Neill, Z. D., & Hu, F. (2021) · Information · doi:10.3390/info12120499 · Cited by 11
Gonzalez-Gomez, R., Ibañez, A., & Moguilner, S. (2023) · Network Neuroscience · doi:10.1162/netn_a_00285 · Cited by 10
Chen, H., Meng, L., Xi, Y., Xu, C., Wang, Y., Xin, M., Chen, G., & Chen, Y. (2023) · Journal of Algorithms & Computational Technology · doi:10.1177/17483026231176196 · Cited by 10
Zhang, W., Li, H., Wang, M., Wen, L., Huang, J., & Yang, L. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.120369 · Cited by 10
Sensitivity analysis on critical combinations of input parameters in DEM granular flow analysis
Xiao, J., Tozato, K., Nomura, R., Otake, Y., Terada, K., & Moriguchi, S. (2024) · Acta Geotechnica · doi:10.1007/s11440-024-02499-2 · Cited by 10
Deep Learning-Based Energy Consumption Prediction Model for Green Industrial Parks
Lai, C., Wang, Y., Zhu, J., & Zhou, X. (2025) · Applied Artificial Intelligence · doi:10.1080/08839514.2025.2462375 · Cited by 10
Chi, P., Zhang, Z., Liang, R., Hu, Y., Ni, K., & Li, W. (2021) · Electrical Engineering · doi:10.1007/s00202-021-01324-3 · Cited by 10
Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities
Duhayyim, M. A., Alabdulkreem, E. A., Tarmissi, K. S., Aljebreen, M., Khier, B. S. I. A. E., Zamani, A. S., Yaseen, I., & Eldesouki, M. I. (2022) · Applied Sciences · doi:10.3390/app122111187 · Cited by 10
Quantitative analysis and spatial distribution of landform spatial structure on Loess Plateau
Lin, S., Chen, N., Qianqian, Z., Feng, Q., Jing, X., Meng, Q., Yugui, F., Zihao, Y., Weibin, L., Deng, J., et al. (2022) · Progress in Physical Geography Earth and Environment · doi:10.1177/03091333221134192 · Cited by 10
Changes in CO2 concentration drive a succession of toxic and non-toxic strains of Microcystis blooms
Jiang, J., Zeng, J., Wang, J., Zuo, J., Wei, N., Song, L., Shan, K., & Gan, N. (2023) · Water Research · doi:10.1016/j.watres.2023.121056 · Cited by 10
A comparison of machine learning methods for estimation of snow density using satellite images
Goodarzi, M. R., Sabaghzadeh, M., Barzkar, A., Niazkar, M., & Saghafi, M. (2024) · Water and Environment Journal · doi:10.1111/wej.12939 · Cited by 10
Qiu, X., Suganthan, P. N., & Amaratunga, A. J. G. (2018) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628724 · Cited by 10
Zheng, X., Wang, H., Jiang, K., & Ben-teng, H. (2019) · Energies · doi:10.3390/en12193793 · Cited by 10
Kong, Z., Zhou, X., Cui, Y., & Lv, H. (2019) · Applied Sciences · doi:10.3390/app9204215 · Cited by 10
Eseye, A. T., Lehtonen, M., Tukia, T., Uimonen, S., & Millar, R. J. (2019) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin41052.2019.8972188 · Cited by 10
Yan, W., Wang, J., Cheng, J., Wan, Z., Xing, K., & Gao, K. (2021) · Geofluids · doi:10.1155/2021/8895844 · Cited by 10
Multi-Variable Time Series Decoding with Long Short-Term Memory and Mixture Attention
Seddik, S., Routaib, H., & Haddadi, A. E. (2023) · Acadlore Transactions on AI and Machine Learning · doi:10.56578/ataiml020304 · Cited by 11
Optimization of Sleep Stage Classification using Single-Channel EEG Signals
Rahman, M. A., Hossain, M. A., Kabir, M. R., Sani, M. H., Abdullah‐Al‐Mamun, & Awal, M. A. (2019) · International Conference on Electrical Information and Communication Technology (EICT) · doi:10.1109/eict48899.2019.9068825 · Cited by 10
Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
Yuan, T., Jiang, D.-S., Huang, S.-Y., Hsu, Y.-Y., Yeh, H.-C., Huang, M. L., & Lu, C. (2021) · Applied Sciences · doi:10.3390/app11135930 · Cited by 10
Hierarchical fault diagnosis and severity identification method of building air condition systems
Yang, C., Liu, Q., Zhang, J., Chen, H., Li, Z., Liu, Z., & Chen, J. (2023) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.121309 · Cited by 9
Risk assessment of imported malaria in China: a machine learning perspective
Yang, S., Li, R., Yan, S.-N., Yang, H.-Y., Cao, Z.-Y., Zhang, L., Xue, J.-B., Xia, Z., Xia, S., & Zheng, B. (2024) · BMC Public Health · doi:10.1186/s12889-024-17929-9 · Cited by 9
Bridge monitoring data processing and prediction based on information entropy
Li, J., Fan, X., & Liu, Y. (2024) · Structures · doi:10.1016/j.istruc.2024.106849 · Cited by 9
Huang, Z., Liu, L., Xiang, J., Deng, C., Yang, Y., Liu, J., & Bao, Y. (2024) · Energy Engineering · doi:10.32604/ee.2024.054514 · Cited by 9
Dhar, S., Heinkelmann, R., Belda, S., Modiri, S., Schuh, H., Guessoum, S., Ferrándiz, J. M., Balasubramanian, N. B., & Dikshit, O. (2024) · Earth Planets and Space · doi:10.1186/s40623-024-02055-y · Cited by 9
Shen, M., Cheng, A., & Bi, Y. (2024) · Design Science · doi:10.1017/dsj.2024.15 · Cited by 9
Umunnawuike, C., Mahat, S. Q. A., Aziz, M. A. B. A., Gbonhinbor, J., Money, B., Nwaichi, P. I., Nyah, F., Abutu, D., Umunnawuike, C., Nwanosike, F. O., et al. (2025) · SPE Nigeria Annual International Conference and Exhibition · doi:10.2118/228640-ms · Cited by 9
Application of Variational Mode Decomposition and Deep Learning in Short-Term Power Load Forecasting
Yu, P., Fang, J., Xu, Y., & Shi, Q. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1883/1/012128 · Cited by 9
Qian, B., & Lu, S. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108571 · Cited by 9
Li, M., Xie, X., & Zhang, D. (2021) · Sustainability · doi:10.3390/su14010188 · Cited by 9
Deep Learning Based Customer Preferences Analysis in Industry 4.0 Environment
Sun, Q., Feng, X., Zhao, S., Cao, H., Li, S., & Yao, Y. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-021-01830-5 · Cited by 10
An Approximate Forecasting of Electricity Load and Price of a Smart Home Using Nearest Neighbor
Nawaz, M., Javaid, N., Mangla, F. U., Munir, M., Ihsan, F., Javaid, A., & Asif, M. S. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-22354-0_46 · Cited by 9
Patro, S. P., Padhy, N., & Sah, R. D. (2023) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.316145 · Cited by 8
Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence
Kuizinienė, D., & Krilavičius, T. (2024) · Electronics · doi:10.3390/electronics13081596 · Cited by 8
Forecasting uranium prices: Some empirical results
Pedregal, D. J. (2019) · Nuclear Engineering and Technology · doi:10.1016/j.net.2019.11.028 · Cited by 8
Xu, J., Yan, Q., Zhang, S., & Wu, C. (2023) · Mathematics · doi:10.3390/math11244994 · Cited by 8
Metadata
- Authors
- Zheng, H., Yuan, J., & Chen, L.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- Corresponding author
- Zheng, Huiting
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2017-08-08
- Volume
- 10
- Issue
- 8
- Pages
- 1168
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2743680082
- Cited by
- 635
- References
- 35
- Citations by year
- 2026: 31 · 2025: 54 · 2024: 77 · 2023: 83 · 2022: 112 · 2021: 111 · 2020: 74 · 2019: 64 · 2018: 25 · 2017: 4
- FWCI
- 18.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Key Research and Development Program of China (No. 2017YFB0802303)
- Application platform and Industrialization for efficient cloud computing for Big data of the Science and Technology Supported Program of Jiangsu Province (BA2015052)
- Funding of National Natural Science Foundation of China (Grant No.61571226)
- Jiangsu Program for the transformation of scientific and technological achievements (BA2015051)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- short-term load forecasting, empirical mode decomposition, long short-term memory, similar day selection, XGBoost, feature importance, intrinsic mode functions, hybrid forecasting model, power system operation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review