Performance Analysis of Time Series Deep Learning Models for Climate Prediction in Indoor Hydroponic Greenhouses at Different Time Intervals
Abstract
:1. Introduction
- the LSTM model outperformed the other models in all time intervals in predicting the temperature and humidity, achieving the lowest MAE, RMSE, SEP, and the highest R-squared values;
- the increase in the time interval adversely affects the performance of the models;
- the DNN model performed better than the 1D-CNN model but not as well as the LSTM model;
- the performance of the models varied for different climate variables, with temperature being the easiest to predict and humidity being the most challenging.
2. Related Work
3. Results and Discussion
4. Materials and Methods
4.1. Greenhouse Measurements and Dataset
4.2. DL Models for Forecasting Environmental Changes
4.3. Performance Metrics
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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DNN-1 | DNN-5 | |||||
---|---|---|---|---|---|---|
Temperature | Humidity | CO2 | Temperature | Humidity | CO2 | |
R2 | 0.98 | 0.96 | 0.81 | 0.99 | 0.95 | 0.97 |
MAE | 0.005 | 0.02 | 0.01 | 0.004 | 0.02 | 0.003 |
RMSE | 0.006 | 0.03 | 0.01 | 0.005 | 0.03 | 0.004 |
SEP (%) | 0.6 | 3.04 | 3.45 | 0.4 | 3.65 | 1.08 |
DNN-10 | DNN-15 | |||||
Temperature | Humidity | CO2 | Temperature | Humidity | CO2 | |
R2 | 0.95 | 0.94 | 0.81 | 0.98 | 0.93 | 0.91 |
MAE | 0.008 | 0.023 | 0.009 | 0.005 | 0.024 | 0.006 |
RMSE | 0.009 | 0.031 | 0.011 | 0.006 | 0.035 | 0.007 |
SEP (%) | 0.88 | 3.8 | 2.99 | 0.56 | 4.22 | 2.15 |
LSTM-1 | LSTM-5 | |||||
---|---|---|---|---|---|---|
Temperature | Humidity | CO2 | Temperature | Humidity | CO2 | |
R2 | 0.99 | 0.96 | 0.96 | 0.99 | 0.94 | 0.93 |
MAE | 0.004 | 0.018 | 0.004 | 0.004 | 0.02 | 0.005 |
RMSE | 0.004 | 0.027 | 0.0045 | 0.005 | 0.032 | 0.006 |
SEP (%) | 0.4 | 3.09 | 1.35 | 0.46 | 3.54 | 1.77 |
LSTM-10 | LSTM-15 | |||||
Temperature | Humidity | CO2 | Temperature | Humidity | CO2 | |
R2 | 0.95 | 0.94 | 0.89 | 0.96 | 0.93 | 0.81 |
MAE | 0.008 | 0.022 | 0.007 | 0.007 | 0.024 | 0.009 |
RMSE | 0.01 | 0.034 | 0.008 | 0.008 | 0.037 | 0.01 |
SEP (%) | 0.94 | 3.85 | 2.38 | 0.84 | 4.18 | 3.17 |
1D-CNN-1 | 1D-CNN-5 | |||||
---|---|---|---|---|---|---|
Temperature | Humidity | CO2 | Temperature | Humidity | CO2 | |
R2 | 0.96 | 0.96 | 0.97 | 0.95 | 0.93 | 0.92 |
MAE | 0.007 | 0.02 | 0.003 | 0.006 | 0.025 | 0.005 |
RMSE | 0.008 | 0.03 | 0.004 | 0.009 | 0.035 | 0.007 |
SEP (%) | 0.79 | 3.46 | 1.1 | 0.69 | 4.2 | 1.84 |
1D-CNN-10 | 1D-CNN-15 | |||||
Temperature | Humidity | CO2 | Temperature | Humidity | CO2 | |
R2 | 0.97 | 0.95 | 0.74 | 0.88 | 0.94 | 0.74 |
MAE | 0.005 | 0.02 | 0.009 | 0.013 | 0.024 | 0.011 |
RMSE | 0.008 | 0.03 | 0.012 | 0.015 | 0.034 | 0.012 |
SEP (%) | 0.56 | 3.48 | 3.02 | 1.45 | 4.21 | 3.95 |
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Eraliev, O.; Lee, C.-H. Performance Analysis of Time Series Deep Learning Models for Climate Prediction in Indoor Hydroponic Greenhouses at Different Time Intervals. Plants 2023, 12, 2316. https://doi.org/10.3390/plants12122316
Eraliev O, Lee C-H. Performance Analysis of Time Series Deep Learning Models for Climate Prediction in Indoor Hydroponic Greenhouses at Different Time Intervals. Plants. 2023; 12(12):2316. https://doi.org/10.3390/plants12122316
Chicago/Turabian StyleEraliev, Oybek, and Chul-Hee Lee. 2023. "Performance Analysis of Time Series Deep Learning Models for Climate Prediction in Indoor Hydroponic Greenhouses at Different Time Intervals" Plants 12, no. 12: 2316. https://doi.org/10.3390/plants12122316