Short-Term Power Load Forecasting Based on DE-IHHO Optimized BiLSTM Article Swipe
Xuelei Liu
,
Ziqi Ma
,
Hanrui Guo
,
Yedong Xu
,
Yingli Cao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3437247
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3437247
Accurate short-term power load forecasting is the key to determining the grid company’s dispatch plan and system operation mode. Aiming at the problem of low prediction accuracy due to the difficulty in selecting hyperparameters of BiLSTM, a hybrid parallel Harris hawk optimization algorithm (DE-IHHO) is proposed to choose the optimal hyperparameters of BiLSTM to improve the prediction accuracy of the model. In this paper, several load forecasting models are tested and BiLSTM with better performance is chosen as the baseline model. Aiming to solve the problem of the complex selection of hyperparameters for BiLSTM, the Harris Hawk Optimization (HHO) algorithm is used to obtain better hyperparameter combinations and improve prediction accuracy. To further explore the optimal hyperparameter combinations of BiLSTM, running in parallel with differential evolutionary algorithm (DE) to enhance the search diversity, adopting chaotic dyadic learning strategy and mutation operation strategy to improve the global search capability of HHO, and finally smoothing the optimal solution to reduce the influence of abnormal solutions. The results show that the convergence speed and optimization ability of DE-IHHO are significantly improved, and the BiLSTM optimized in this way improves considerably in all three metrics of MAE, MAPE, and RMSE, proving this prediction model’s effectiveness.
Related Topics
Concepts
Hyperparameter
Hyperparameter optimization
Computer science
Convergence (economics)
Smoothing
Artificial intelligence
Term (time)
Differential evolution
Electric power system
Key (lock)
Particle swarm optimization
Artificial neural network
Mathematical optimization
Machine learning
Power (physics)
Mathematics
Support vector machine
Economics
Economic growth
Computer security
Computer vision
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3437247
- OA Status
- gold
- Cited By
- 6
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401246679
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401246679Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2024.3437247Digital Object Identifier
- Title
-
Short-Term Power Load Forecasting Based on DE-IHHO Optimized BiLSTMWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Xuelei Liu, Ziqi Ma, Hanrui Guo, Yedong Xu, Yingli CaoList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3437247Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2024.3437247Direct OA link when available
- Concepts
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Hyperparameter, Hyperparameter optimization, Computer science, Convergence (economics), Smoothing, Artificial intelligence, Term (time), Differential evolution, Electric power system, Key (lock), Particle swarm optimization, Artificial neural network, Mathematical optimization, Machine learning, Power (physics), Mathematics, Support vector machine, Economics, Economic growth, Computer security, Computer vision, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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