The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
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Cites
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN
Shi, H., Xu, M., & Li, R. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2686012 · Cited by 1,102
Deep learning based ensemble approach for probabilistic wind power forecasting
Wang, H.-Z., Li, G.-Q., Wang, G.-B., Peng, J.-C., Jiang, H., & Liu, Y.-T. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.11.111 · Cited by 753
Self-citationA short-term building cooling load prediction method using deep learning algorithms
Fan, C., Xiao, F., & Zhao, Y. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.03.064 · Cited by 692
On recent advances in PV output power forecast
Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544
Deep belief network based deterministic and probabilistic wind speed forecasting approach
Wang, H. Z., Wang, G. B., Li, G. Q., Peng, J. C., & Liu, Y. T. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.08.108 · Cited by 495
Self-citationA Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output
Yang, H.-T., Huang, C.-M., Huang, Y.-C., & Pai, Y.-S. (2014) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2014.2313600 · Cited by 469
Transfer learning for short-term wind speed prediction with deep neural networks
Hu, Q., Zhang, R., & Zhou, Y. (2016) · Renewable Energy · doi:10.1016/j.renene.2015.06.034 · Cited by 464
Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting
Qiu, X., Ren, Y., Suganthan, P. N., & Amaratunga, G. A. J. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.01.015 · Cited by 450
An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data
Liu, J., Fang, W., Zhang, X., & Yang, C. (2015) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2014.2381224 · Cited by 349
Solar Power Prediction Based on Satellite Images and Support Vector Machine
Jang, H. S., Bae, K. Y., Park, H.-S., & Sung, D. K. (2016) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2016.2535466 · Cited by 340
Cited by
Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109792 · Cited by 1,259
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258
Solar photovoltaic generation forecasting methods: A review
Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.11.019 · Cited by 990
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Mosavi, A., Salimi, M., Faizollahzadeh Ardabili, S., Rabczuk, T., Shamshirband, S., & Varkonyi-Koczy, A. R. (2019) · Energies · doi:10.3390/en12071301 · Cited by 578
Wang, K., Qi, X., & Liu, H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113315 · Cited by 574
Metadata
- Authors
- Wang, H., Yi, H., Peng, J., Wang, G., Liu, Y., Jiang, H., & Liu, W.
- Venue
- Energy Conversion and Management
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 153
- Pages
- 409-422
- ISSN
- 0196-8904 (ISSN-L)
- OpenAlex
- W2763128055
- Cited by
- 406
- References
- 38
- FWCI
- 17.55
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Wang, H., Yi, H., Peng, J., Wang, G., Liu, Y., Jiang, H., & Liu, W. (2017). Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2017.10.008
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
