The DOI matches its Crossref record; 43 of the 47 references it cites were found in a registry or an index, and 2 are not checked yet.
Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods
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“ANN [14], RNN [25], DNN [26], and CNN [27] are applied to the prediction model of MPC.”
From Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC (2023) · cites this paper as [26]
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Proceedings of the 24th international conference on Machine learning
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Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648
Cited by
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Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
Cai, M., Pipattanasomporn, M., & Rahman, S. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.12.042 · Cited by 487
“With the huge success DL techniques made in solving complex statistical problems, researchers also started searching for the DL-driven solutions for load forecasting applications [29,30].”
MethodUltra-Short-Term Industrial Power Demand Forecasting Using LSTM Based Hybrid Ensemble Learning
Tan, M., Yuan, S., Li, S., Su, Y., Li, H., & He, F. H. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2963109 · Cited by 356
“Insometime-consumedforecastingtasks,automated featureselectionusuallygivesacomputationallyefficientmodel with higher accuracy than the arbitrarily chosen features [27].”
BackgroundTien, P. W., Wei, S., Darkwa, J., Wood, C. J., & Calautit, J. K. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100198 · Cited by 332
“While many of the works in the literature predicted electricity consumption of buildings, the study [156] extended deep learning methods to the field of heat load forecasting.”
BackgroundTan, Z., De, G., Li, M., Lin, H., Yang, S., Huang, L., & Tan, Q. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119252 · Cited by 253
Memarzadeh, G., & Keynia, F. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106995 · Cited by 255
Metadata
- Authors
- Suryanarayana, G., Lago, J., Geysen, D., Aleksiejuk, P., & Johansson, C.
- Affiliations
- Flemish Institute for Technological Research (BE)
- Warsaw University of Technology (PL)
- Corresponding author
- Suryanarayana, Gowri
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 157
- Pages
- 141-149
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2804243520
- Cited by
- 155
- References
- 47
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 11 · 2023: 16 · 2022: 24 · 2021: 29 · 2020: 29 · 2019: 18 · 2018: 6
- FWCI
- 7.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- European Union's Horizon 2020 (649743)
- Marie Skodowska-Curie (675318)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Keywords
- heat load forecasting, district heating networks, feature selection, deep learning, ridge regression, LASSO, linear regression, nonlinear dependencies, day-ahead forecasting, heat load
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