Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods
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ANN, RNN, DNN, and CNN are applied to the prediction model of MPC.
“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]
Section 3.4 describes the paper's use of a standard DNN for forecasting, supporting the DNN portion attributed to citation [26]. The paper does not describe applying that forecaster to an MPC prediction model. No page number is available in our copy. (LLM, conf=0.85)
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“…this issue, a recent research trend has been established which adopts Deep Learning (DL) techniques in load prediction and electricity price forecasting (Shi et al., 2018; Rahman et al., 2018; Mocanu et al., 2016; Suryanarayana et al., 2018; Zhang et al., 2015; Atef & Eltawil, 2019a, 2019b, 2020).”
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Sun, C., Liu, Y., Cao, S., Gao, X., Xia, G., Qi, C., & Wu, X. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.06.031 · Cited by 27 · see citing works
The new hybrid approaches to forecasting short-term electricity load
Fan, G., Liu, Y., Hui-zhen, W., Yu, M., & Li, Y.-H. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108759 · Cited by 27 · see citing works
A Novel Combined Model for Heat Load Prediction in District Heating Systems
Wang, Y., Li, Z., Liu, J., Zhao, Y., & Sun, S. (2022) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2023.120372 · Cited by 27 · see citing works
Salehi, S., Kavgic, M., Bonakdari, H., & Begnoche, L. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100343 · Cited by 27 · see citing works
Testing and performance evaluation of the STORM controller in two demonstration sites
Oevelen, T. V., Vanhoudt, D., Johansson, C., & Smulders, E. (2020) · Energy · doi:10.1016/j.energy.2020.117177 · Cited by 26 · see citing works
Self-citationCerquitelli, T., Malnati, G., & Apiletti, D. (2019) · Energies · doi:10.3390/en12152933 · Cited by 25 · see citing works
“Finally, in [33] the predictions of three energy consumption linear models are compared on two Swedish cities.”
MethodSun, C., Liu, Y., Gao, X., Wang, J., Yang, L., & Qi, C. (2022) · Energy · doi:10.1016/j.energy.2022.123214 · Cited by 25 · see citing works
Zhou, M., Yu, J., Sun, F., & Wang, M. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107618 · Cited by 25 · see citing works
Identification method of market power abuse of generators based on lasso-logit model in spot market
Sun, B., Deng, R., Ren, B., Teng, M., Cheng, S., & Wang, F. (2021) · Energy · doi:10.1016/j.energy.2021.121634 · Cited by 24 · see citing works
Frison, L., Gölzhäuser, S., Bitterling, M., & Kramer, W. (2024) · Energy · doi:10.1016/j.energy.2024.132745 · Cited by 24 · see citing works
“An important work was published by Suryanarayana et al. [26], who compared different linear, ridge and lasso regression in addition to automatic feature selection and a quite simple FFNN with two hidden layers for thermal load forecasting in heating networks.”
BackgroundAdaptive Feature Selection for Probabilistic Multi-Energy Load Forecasting
Ge, Y., Zhang, W., Liu, G., Li, Z., & Li, H. (2023) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2023.3344540 · Cited by 23 · see citing works
“[22] investigated feature selection method for heating load forecasting.”
A data-driven model for thermodynamic properties of a steam generator under cycling operation
Navarkar, A., Hasti, V. R., Deneke, E., & Gore, J. P. (2020) · Energy · doi:10.1016/j.energy.2020.118973 · Cited by 23 · see citing works
Systematic review on uncertainty quantification in machine learning-based building energy modeling
Xu, X., Hu, Y., Atamturktur, S., Chen, L., & Wang, J. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115817 · Cited by 21 · see citing works
Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting
Li, B., Shao, Y., Lian, Y., Li, P., & Lei, Q. (2023) · Energies · doi:10.3390/en16176234 · Cited by 20 · see citing works
Wang, C., Yuan, J., Zhang, J., Deng, N., Zhou, Z., & Gao, F. (2020) · Energy · doi:10.1016/j.energy.2020.117714 · Cited by 20 · see citing works
Saletti, C., Morini, M., & Gambarotta, A. (2020) · Energies · doi:10.3390/en13112835 · Cited by 20 · see citing works
“Apart from the already cited STORM project [33], other interesting outputs are as follows: Figure 9.”
BackgroundInfluentialBo, Z., Li, H., Yang, H., Li, C., Wu, S., Xu, C., Xiong, G., Mariotti, D., Yan, J., Cen, K., et al. (2021) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2021.121075 · Cited by 19 · see citing works
“A number of different architectures of neural networks have been evaluated for predicting thermal behavior in supercapacitors, such as standard deep neural networks (DNN) [27, 28], deep recurrent neural networks (RNN) [29], conditional generative adversarial neural networks (cGAN) [30], and deep feedforward neural net…”
BackgroundZhao, H., & Guo, S. (2021) · Mathematics · doi:10.3390/math9141645 · Cited by 19 · see citing works
“[13] selected the last 7 days of heat load and the next 24 h temperature prediction results as the heat load forecasting input variables.”
BackgroundWang, Y., Wang, H., Meng, X., Dong, H., Chen, X., Xiang, H., & Xing, J. (2023) · Energy · doi:10.1016/j.energy.2023.129387 · Cited by 19 · see citing works
Heating Demand Forecasting with Multiple Regression: Model Setup and Case Study
Baltputnis, K., Petrichenko, R., & Sobolevsky, D. (2018) · IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) · doi:10.1109/aieee.2018.8592144 · Cited by 17 · see citing works
A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction
Wang, D., Guo, J., & Zhang, C. (2024) · Advances in Civil Engineering · doi:10.1155/2024/8163062 · Cited by 16 · see citing works
A Parallel Electrical Optimized Load Forecasting Method Based on Quasi-Recurrent Neural Network
Yang, C., Wang, W., Zhang, X., Guo, Q., Zhu, T., & Ai, Q. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/696/1/012040 · Cited by 16 · see citing works
“Typical methods include Deep Neural Network (DNN)[6], Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), etc.”
MethodXue, G., Zhang, Y., Yu, S.-A., Song, J., Bian, T., Gao, Y., Yan, W., & Guo, Y. (2023) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2023.102005 · Cited by 15 · see citing works
Richter, L., Lenk, S., & Bretschneider, P. (2024) · Smart Cities · doi:10.3390/smartcities7040082 · Cited by 12 · see citing works
Exploring the impact of data uncertainty on the performance of a demand response program
Azari, D., Torbaghan, S. S., Cappon, H. J., Keesman, K. J., Rijnaarts, H. H. M., & Gibescu, M. (2019) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2019.100262 · Cited by 14 · see citing works
Heat demand prediction: A real-life data model vs simulated data model comparison
Naik, K., & Ianakiev, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.093 · Cited by 13 · see citing works
“Suryanarayana et al. investigated in [12] heat prediction investigated Long Short-Term Memory (LSTM) model, and Feature Fusion Long Short-Term Memory (FFLSTM).”
MethodTrabert, U., Pag, F., Orozaliev, J., Jordan, U., & Vajen, K. (2024) · Energy · doi:10.1016/j.energy.2024.131690 · Cited by 12 · see citing works
Heating energy consumption prediction based on improved GA-BP neural network model
Jie, P., Zhou, Y. J., Zhang, Z., & Wei, F. (2025) · Energy · doi:10.1016/j.energy.2025.136392 · Cited by 12 · see citing works
Perspectives on energy efficiency and smart energy systems from the 5th SESAAU2019 conference
Nielsen, T. B., Lund, H., Østergaard, P. A., Duić, N., & Mathiesen, B. V. (2020) · Energy · doi:10.1016/j.energy.2020.119260 · Cited by 12 · see citing works
Forecasting of Heat Production in Combined Heat and Power Plants Using Generalized Additive Models
Bujalski, M., & Madejski, P. (2021) · Energies · doi:10.3390/en14082331 · Cited by 12 · see citing works
“In recent years, there has been a clear increase in interest in the use of machine learning methods, such as support vector machines (SVM) [17], random forest [18], deep learning [19] and gradient boosting [20].”
BackgroundA Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps
Hwang, J. K., Duhirwe, P. N., Yun, G. Y., Lee, S., Seo, H., Kim, I., & Santamouris, M. (2020) · Sustainability · doi:10.3390/su12072914 · Cited by 12 · see citing works
“L1 and L2 enforce penalties and modify the loss function by minimizing them [44,45].”
BackgroundShort-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method
Kondaiah, V. Y., & Saravanan, B. (2022) · Energies · doi:10.3390/en15145299 · Cited by 11 · see citing works
Data-driven analysis and prediction of indoor characteristic temperature in district heating systems
Wang, Y., Li, Z., Liu, J., Pei, M., Zhao, Y., & Lu, X. (2023) · Energy · doi:10.1016/j.energy.2023.129023 · Cited by 11 · see citing works
Sleiman, A., & Su, W. (2024) · Energies · doi:10.3390/en17061433 · Cited by 11 · see citing works
“Artificial neural networks (ANNs) can be used to create models for complex nonlinear processes using input variables that are correlated, without making any assumptions about the underlying process [15]; thus, these models were explored for solar power forecasting and are applied in [16–18].”
MethodLoad Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment
Zhang, K., Guo, W., Feng, J., & Liu, M. (2021) · Scientific Programming · doi:10.1155/2021/3250732 · Cited by 11 · see citing works
An exploration of shared heat storage systems in the greenhouse horticulture industry
Ridder, F. D., van Roy, J., de Schutter, B., & Mazairac, W. (2021) · Energy · doi:10.1016/j.energy.2021.121425 · Cited by 10 · see citing works
Benefits of thermal load forecasts in balancing load fluctuations through thermal storage
Ghilardi, A., Frate, G. F., Tucci, M., Bravi, M., Leo, R., & Ferrari, L. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107929 · Cited by 10 · see citing works
Real-time grid optimisation through digitalisation – results of the STORM project
Johansson, C., Vanhoudt, D., Brage, J., & Geysen, D. (2018) · Energy Procedia · doi:10.1016/j.egypro.2018.08.189 · Cited by 10 · see citing works
Self-citationForecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine
Acquaviva, A., Apiletti, D., Attanasio, A., Baralis, E., Bottaccioli, L., Cerquitelli, T., Chiusano, S., Macii, E., & Patti, E. (2019) · Electronics · doi:10.3390/electronics8050491 · Cited by 10 · see citing works
“[41] investigate the performance of three different linear models to forecast energy consumption: (i) linear, (ii) ridge, and (iii) lasso regression.”
BackgroundTemporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction
Gui, N., Lou, J., Qiu, Z., & Gui, W. (2019) · Processes · doi:10.3390/pr7070473 · Cited by 9 · see citing works
“The selection of the most related features from a large variety of sensors is important for the realization of effective control [8].”
BackgroundCombining Deep Neural Network and Fourier Series for Tourist Arrivals Forecasting
Shabri, A., Samsudin, R., & Yusoff, Y. B. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/864/1/012094 · Cited by 9 · see citing works
- 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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