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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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Cited by
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Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
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“Insometime-consumedforecastingtasks,automated featureselectionusuallygivesacomputationallyefficientmodel with higher accuracy than the arbitrarily chosen features [27].”
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“While many of the works in the literature predicted electricity consumption of buildings, the study [156] extended deep learning methods to the field of heat load forecasting.”
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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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