Cited by 155Integrated Energy Systems OptimizationEnergy Load and Power ForecastingBuilding Energy and Comfort Optimization

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Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

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Suryanarayana, Gowri, Lago, Jesus, Geysen, Davy, Aleksiejuk, Piotr, Johansson, Christian·2018·Energy

DOI 10.1016/j.energy.2018.05.111

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Background
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BackgroundInfluential
Combinatorial atomistic-to-AI prediction and experimental validation of heating effects in 350 F supercapacitor modules
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Background
Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine
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Background
Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast
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“Typical methods include Deep Neural Network (DNN)[6], Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), etc.”
Method
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“Suryanarayana et al. investigated in [12] heat prediction investigated Long Short-Term Memory (LSTM) model, and Feature Fusion Long Short-Term Memory (FFLSTM).”
Method
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“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].”
Background
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Background
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Method
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Self-citation
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Background
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Gui, N., Lou, J., Qiu, Z., & Gui, W. (2019) · Processes · doi:10.3390/pr7070473 · Cited by 9
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Background
Combining 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

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
ORCID: Suryanarayana, Gowri
Lago, Jesus
Geysen, Davy
Venue: Energy
Publisher: Elsevier BV
Published: 2018-08
Volume: 157
Pages: 141-149
ISSN: 0360-5442 (ISSN-L)
DOI: 10.1016/j.energy.2018.05.111
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

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