The DOI matches its Crossref record; 139 of the 139 references it cites were found in a registry or an index.
Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review
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Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
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A Survey on Multi-Task Learning
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Cited by
A comprehensive review on deep learning approaches for short-term load forecasting
Eren, Y., & Küçükdemiral, İ. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114031 · Cited by 303
Metadata
- Authors
- Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S.
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 115
- Pages
- 105287
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W4290990408
- Cited by
- 299
- References
- 139
- FWCI
- 8.41
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review. https://app.cochat.ai/papers/10.1016/j.engappai.2022.105287
- The paper
- Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. (2022). Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2022.105287
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.
