Ultra-short-term Photovoltaic Power Prediction Based on Multi-head ProbSparse Self-attention and Long Short-term Memory Article Swipe

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YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1088/1742-6596/2558/1/012007

To provide accurate predictions of photovoltaic (PV) power generation, an MHPSA-LSTM ultra-short-term multipoint PV power prediction model combining Multi-head ProbSparse self-attention (MHPSA) and long short-term memory (LSTM) network is posited. The MHPSA is first used to capture information dependencies at a distance. Secondly, the LSTM is used to enhance the local correlation. At last, a pooling layer is added after LSTM to reduce the parameters of the fully-connected layer and alleviate overfitting, thus improving the prediction accuracy. The MHPSA-LSTM model is validated on a PV plant at the Desert Knowledge Australia Solar Centre as an example, and the RMSE, MAE, and R 2 of MHPSA-LSTM are 0.527, 0.264, and 0.917, respectively. MHPSA-LSTM has higher prediction accuracy compared with BP, LSTM, GRU, and CNN-LSTM.

Related Topics

Overfitting

Computer Science

Photovoltaic System

Artificial Intelligence

Long Short-Term Memory

Machine Learning

Recurrent Neural Network

Electrical Engineering

Concepts

Overfitting Pooling Computer science Term (time) Photovoltaic system Artificial intelligence Layer (electronics) Long short term memory Power (physics) Machine learning Pattern recognition (psychology) Artificial neural network Recurrent neural network Engineering Organic chemistry Physics Quantum mechanics Chemistry Electrical engineering

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Type: article
Language: en
Landing Page: https://doi.org/10.1088/1742-6596/2558/1/012007
PDF: https://iopscience.iop.org/article/10.1088/1742-6596/2558/1/012007/pdf
OA Status: diamond
Cited By: 2
References: 7
Related Works: 10
OpenAlex ID: https://openalex.org/W4385707331

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OpenAlex ID: https://openalex.org/W4385707331

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DOI: https://doi.org/10.1088/1742-6596/2558/1/012007

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Title: Ultra-short-term Photovoltaic Power Prediction Based on Multi-head ProbSparse Self-attention and Long Short-term Memory

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-08-01

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Authors: T. Yang, Quanming Zhao, Yifan Meng

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Landing page: https://doi.org/10.1088/1742-6596/2558/1/012007

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PDF URL: https://iopscience.iop.org/article/10.1088/1742-6596/2558/1/012007/pdf

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Concepts: Overfitting, Pooling, Computer science, Term (time), Photovoltaic system, Artificial intelligence, Layer (electronics), Long short term memory, Power (physics), Machine learning, Pattern recognition (psychology), Artificial neural network, Recurrent neural network, Engineering, Organic chemistry, Physics, Quantum mechanics, Chemistry, Electrical engineering

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Cited by: 2

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Citations by year (recent): 2025: 1, 2024: 1

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abstract_inverted_index.CNN-LSTM.	123
abstract_inverted_index.Knowledge	90
abstract_inverted_index.Secondly,	43
abstract_inverted_index.accuracy.	77
abstract_inverted_index.alleviate	71
abstract_inverted_index.combining	18
abstract_inverted_index.distance.	42
abstract_inverted_index.improving	74
abstract_inverted_index.validated	82
abstract_inverted_index.MHPSA-LSTM	11, 79, 105, 112
abstract_inverted_index.Multi-head	19
abstract_inverted_index.ProbSparse	20
abstract_inverted_index.multipoint	13
abstract_inverted_index.parameters	65
abstract_inverted_index.prediction	16, 76, 115
abstract_inverted_index.short-term	25
abstract_inverted_index.generation,	9
abstract_inverted_index.information	38
abstract_inverted_index.predictions	4
abstract_inverted_index.correlation.	52
abstract_inverted_index.dependencies	39
abstract_inverted_index.overfitting,	72
abstract_inverted_index.photovoltaic	6
abstract_inverted_index.respectively.	111
abstract_inverted_index.self-attention	21
abstract_inverted_index.fully-connected	68
abstract_inverted_index.ultra-short-term	12
cited_by_percentile_year.max	95
cited_by_percentile_year.min	90
countries_distinct_count	1
institutions_distinct_count	3
sustainable_development_goals[0].id	https://metadata.un.org/sdg/7
sustainable_development_goals[0].score	0.7799999713897705
sustainable_development_goals[0].display_name	Affordable and clean energy
citation_normalized_percentile.value	0.56841142
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False