Incremental Deep-Learning for Continuous Load Prediction in Energy Management Systems Article Swipe
Gustavo Aragón
,
Harsh Puri
,
Alexander Graß
,
Sisay Adugna Chala
,
Christian Beecks
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/ptc.2019.8810793
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/ptc.2019.8810793
In this work, we introduce load prediction as continuous input for optimization models within an optimization framework for short-term control of complex energy systems. In this context, we investigated long short-term memory (LSTM) models for load prediction, because they allow incremental training in an application with continuous real-time data and have not been used in other works for continuous load prediction to our knowledge. The test and evaluation were realized using data sets of real residential data from different locations in different time resolution - hourly and minutely. Accordingly, we tested different recurrent neural network (RNN) parameters of the model such as the number of layers, the number of hidden nodes, the inclusion of regularization, and dropout in order to find the optimal LSTM configuration for our continuous load prediction application. Besides, we analyzed the quality of the LSTM algorithm by comparing it in continuous mode with the baseline model and in batch mode with the statistical model ARIMA. Training and prediction time, as well as the error stabilization time were parameters used for the evaluation. The results showed that LSTM algorithms are highly promising for integrating continuous load prediction with incremental learning.
Related Topics
Concepts
Computer science
Dropout (neural networks)
Autoregressive integrated moving average
Artificial neural network
Regularization (linguistics)
Context (archaeology)
Artificial intelligence
Machine learning
Recurrent neural network
Time series
Energy (signal processing)
Term (time)
Data mining
Biology
Paleontology
Statistics
Quantum mechanics
Physics
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ptc.2019.8810793
- OA Status
- green
- Cited By
- 14
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2971208531
All OpenAlex metadata
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https://openalex.org/W2971208531Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ptc.2019.8810793Digital Object Identifier
- Title
-
Incremental Deep-Learning for Continuous Load Prediction in Energy Management SystemsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-06-01Full publication date if available
- Authors
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Gustavo Aragón, Harsh Puri, Alexander Graß, Sisay Adugna Chala, Christian BeecksList of authors in order
- Landing page
-
https://doi.org/10.1109/ptc.2019.8810793Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://zenodo.org/record/3386447Direct OA link when available
- Concepts
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Computer science, Dropout (neural networks), Autoregressive integrated moving average, Artificial neural network, Regularization (linguistics), Context (archaeology), Artificial intelligence, Machine learning, Recurrent neural network, Time series, Energy (signal processing), Term (time), Data mining, Biology, Paleontology, Statistics, Quantum mechanics, Physics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2, 2023: 2, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | 2019 IEEE Milan PowerTech |
| primary_location.landing_page_url | https://doi.org/10.1109/ptc.2019.8810793 |
| publication_date | 2019-06-01 |
| publication_year | 2019 |
| referenced_works | https://openalex.org/W2801908035, https://openalex.org/W2774966631, https://openalex.org/W2064675550, https://openalex.org/W2122825543, https://openalex.org/W1815076433, https://openalex.org/W2904391972, https://openalex.org/W2047484385, https://openalex.org/W2215699581, https://openalex.org/W2129697104, https://openalex.org/W2073278642, https://openalex.org/W2108067853, https://openalex.org/W2095705004, https://openalex.org/W1503398984, https://openalex.org/W2313953460, https://openalex.org/W2592023122, https://openalex.org/W2754252319, https://openalex.org/W2087347434, https://openalex.org/W1984755515 |
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