ArticleHeliyon2023
Prediction of solar irradiance using convolutional neural network and attention mechanism-based long short-term memory network based on similar day analysis and an attention mechanism.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Comparative analysis of CNN-LSTM and CNN-BiLSTM hybrid deep learning models for solar radiation prediction.Scientific reports · 2026Article
- Deep FS: A Deep Learning Approach for Surface Solar Radiation.Sensors (Basel, Switzerland) · 2024Article
- Attention-Based Models for Multivariate Time Series Forecasting: Multi-step Solar Irradiation Prediction.Heliyon · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As one of the future's most promising clean energy sources, solar energy is the key to developing renewable energy. The randomness of solar irradiance can affect the efficiency of photovoltaic power generation, which makes photovoltaic power generation planning extremely difficult. The main goal of this study is to accurately predict solar irradiance and establish a prediction model with meteorological characteristics to improve prediction accuracy. This paper proposes a convolutional neural network (CNN) and attention mechanism-based long short-term memory network (A-LSTM) to predict solar irradiance the next day. In addition, the prediction accuracy is further improved by combining similar day analyses. A similar day prediction model is constructed by selecting solar energy data from Andhra Pradesh, India. The experimental results show that the method proposed in this paper can predict solar irradiance more accurately, providing a new idea for photovoltaic power generation planning.
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Registered trials
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