The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
“Currently, simple on/off control or proportional-integral-derivative (PID) controllers are widely used in heating, ventilation, and air conditioning (HVAC) systems [5,6].”
From Research on Energy-Saving Optimization of Mushroom Growing Control Room Based on Neural Network Model Predictive Control (2025) · cites this paper as [5]
Citations
Cites
Optimum Settings for Automatic Controllers
Ziegler, J. G., & Nichols, N. B. (1942) · Transactions of the American Society of Mechanical Engineers · doi:10.1115/1.4019264 · Cited by 5,439
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1023/a:1022676722315 · Cited by 3,907
Addressing Function Approximation Error in Actor-Critic Methods
Fujimoto, S., van Hoof, H., & Meger, D. (2018) · arXiv · doi:10.48550/arxiv.1802.09477 · Cited by 3,650
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
Dueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120
Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342
Artificial Intelligence: A Modern Approach
Ligeza, A. (1995) · Neurocomputing · doi:10.1016/0925-2312(95)90020-9 · Cited by 1,857
Rainbow: Combining Improvements in Deep Reinforcement Learning
Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11796 · Cited by 1,846
All you need to know about model predictive control for buildings
Drgoňa, J., Arroyo, J., Cupeiro Figueroa, I., Blum, D., Arendt, K., Kim, D., Ollé, E. P., Oravec, J., Wetter, M., Vrabie, D. L., et al. (2020) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2020.09.001 · Cited by 1,020
Research works printed but not linked: 4
- Abbas, 2023, A review of adaptive tuning of PID-controller: Optimization techniques and applications, Int. J. Nonlinear Anal. Appl. In Press, 6822, 2008
- Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016). OpenAI Gym. http://arxiv.org/abs/1606.01540. · arXiv
- Gao, G., Li, J., & Wen, Y. (2019). Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning. http://arxiv.org/abs/1901.04693. · arXiv
- Ogunlowo, 2022, Effect of envelope characteristics on the accuracy of discretised greenhouse model in TRNSYS, Journal of Agricultural Engineering, 53
Cited by
Song, Y., Zheng, W., Guo, G., Wang, M., Luo, C., Chen, C., & Li, Z. (2025) · Energies · doi:10.3390/en18205550 · Cited by 3
Metadata
- Authors
- Adesanya, M. A., Obasekore, H., Rabiu, A., Na, W.-H., Ogunlowo, Q. O., Akpenpuun, T. D., Kim, M.-H., Kim, H.-T., Kang, B.-Y., & Lee, H.-W.
- Affiliations
- Kyungpook National University (KR)
- University of Ilorin (NG)
- Gyeongsang National University (KR)
- Corresponding author
- Lee, Hyun-Woo
- ORCID
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 252
- Pages
- 124126
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4395956864
- Cited by
- 72
- References
- 50
- Citations by year
- 2026: 31 · 2025: 32 · 2024: 9
- FWCI
- 17.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- National Research Foundation of Korea
- Korea Institute of Planning and Evaluation for Technology in Food Agriculture Forestry and Fisheries
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Greenhouse Technology and Climate Control
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- deep reinforcement learning, PID controller tuning, energy optimization, indoor temperature control, tomato greenhouse, reference tracking, Ziegler-Nichols tuning, energy consumption reduction
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach. https://app.cochat.ai/papers/10.1016/j.eswa.2024.124126
- The paper
- Adesanya, M. A., Obasekore, H., Rabiu, A., Na, W.-H., Ogunlowo, Q. O., Akpenpuun, T. D., Kim, M.-H., Kim, H.-T., Kang, B.-Y., & Lee, H.-W. (2024). Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2024.124126
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.
