ArticleScientific reports2023
Temperature prediction of solar greenhouse based on NARX regression neural network.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Strict Recursive Closed-Loop Evaluation of Greenhouse Air Temperature Forecasting with Generated Exogenous Variables and Exogenous-Error Propagation Analysis.Sensors (Basel, Switzerland) · 2026Article
- Physics-Informed Directed Graph Network-Based Temperature Forecasting Model.Sensors (Basel, Switzerland) · 2025Article
- Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model.Frontiers in plant science · 2025Article
- Probabilistic sunspot predictions with a gated recurrent units-based combined model guided by pinball loss.Scientific reports · 2024Article
- Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Temperature has an important influence on plant growth and development. In protected agriculture production, accurate prediction of temperature environment is of great significance. However, due to the time series, nonlinear and multi coupling characteristics of temperature, it is difficult to achieve accurate prediction. We proposed a method for building a solar greenhouse temperature prediction model based on a timeseries analysis, that considers the time series characteristics and dynamic temperature changes in the greenhouse system. The method would predict the temperature of greenhouse, and provide reference for the temperature change law in protected agriculture. A parameter analysis was performed on the nonlinear autoregressive exogenous (NARX) neural network, and a solar greenhouse temperature time series prediction model was established using the NARX regression neural network. The results showed that the proposed model depicted a maximum absolute error of 0.67 °C, and model correlation coefficient of 0.9996. Compared with the wavelet and BP neural networks, the NARX regression neural network accurately predicted and significantly outperformed in the solar greenhouse temperature prediction model. Moreover, the prediction model can accurately predict temperature trends within the solar greenhouse and is pivotal to obtaining precise control of solar greenhouse temperature.
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Registered trials
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