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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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Items 101–155 of 155
Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC
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“In [15, 31] eXtreme Gradient Boosting (XGBoost), linear regression and Deep Neural Networks are compared for thermal load forecasting in DHN.”
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“Multiple-equation linear models with one equation for each hours of the day are built in [8] and [9] but those equations are not merged as we propose in this paper to improve model parsimony.”
BackgroundMethodFeature selection for specific prediction targets at the user level in a district heating network
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Deep Learning Method For Load Forecast In Smart Solar System Based Long Short-Term Memory
Kim, K. N. L., Quoc, T. N., & Thanh, P. N. (2024) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap64064.2024.10710960 · Cited by 6
“Gowri et al. utilized deep learning methodology to predict thermal load based on advanced feature selection, achieving computationally efficiently with higher accuracy [21].”
BackgroundJarral, M. S., Amber, K. P., Cheema, T. A., & Hussain, M. I. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.111800 · Cited by 5
A Forecasting Method of District Heat Load Based on Improved Wavelet Neural Network
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“Meanwhile, the most influential article from Poland introduces advanced methods for heat load forecasting in district heating networks, demonstrating their effectiveness for day-ahead forecasting (Suryanarayana et al., 2018).”
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“An advanced deep-learning algorithm was explored in (Suryanarayana et al. 2018), but it tends to aggravate the complexity of the original model.”
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“Emergent, innovative predictive techniques are deep-learning methods and ensemble learning approaches (Suryanarayana et al., 2018).”
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“The research titled Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods is conducted in Europe by [11] with linear models and deep learning algorithm.”
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“Suryanarayana et al.[9] proposed a prediction model based on deep learning.”
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“Coupling ofMeteorological Factors and Thermal Load There is generally a strong coupling characteristic between meteorological factors and thermalload[12].”
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Optimising Heat Consumption at Micro-Level Using User-Centric Data-Driven ModelPreprint
Naik, K., & Ianakiev, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4108849
A Deep Learning Approach for Enhanced Power Management using Artificial Intelligence
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“…effective features from high-dimensional data without manual feature selection, making it well-suited for mining key genes in biological processes(Wu and Chen, 2015; Sau and Balasubramanian, 2016; Suryanarayana et al., 2018; Sakellaropoulos et al., 2019; Pacal et al., 2020; Saxe et al., 2020).”
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“Suryanarayana et al. [20] demonstrated the superiority of deep FFNNs over linear models in handling higher-dimensional inputs.”
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Investigation of Different Feature Selection Methods for Virtual Sensors of District Heating Systems
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“Historical load data reflect the temporal characteristics of energy usage and provide valuable reference information for future load prediction (Suryanarayana et al. 2018).”
A heat load prediction method for district heating system based on SSA-VMD-TCN-GRU model
Liu, S., Shi, X., Xia, X., Guo, X., & ZHANG, C. (2026) · Science and Technology for the Built Environment · doi:10.1080/23744731.2026.2672885
“…learning algorithms has been applied to heat load forecasting, including Support Vector Machines (SVM) (Proti � c et al. 2015), Decision Trees (DT) (Zhou et al. 2021), Extreme Gradient Boosting (XGBoost) (Suryanarayana et al. 2018), and Artificial Neural Networks (ANN) (Ben Taieb et al. 2012), etc.”
Liu, D., Doskeyeva, G. Z., & Kusmoldayeva, Z. (2026) · International Conference on Cybersecurity, Digital Forensics, and AI Applications (ICCSDFAI) · doi:10.1109/iccsdfai70505.2026.11648011
“Saloux and Candanedo benchmark several algorithms for district-heating demand prediction [18]; Suryanarayana et al. combine deep learning with feature selection for thermal-load forecasting [19]; and Bu¨nning et al. demonstrate that online correction can improve day-ahead heating forecasts [20].”
A deep learning framework for the analysis of demand response strategies in thermal source networksPreprint
Etemad, A., Finn, D., Norouzzadeh, M., Hewitt, N., & O’Donnell, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7450639
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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