Enterprise AI Analysis
Deep learning approaches for predicting solar radiation and freshwater yield in modified pyramid solar still
This analysis provides a comprehensive overview of how deep learning models can predict solar radiation and freshwater yield from modified pyramid solar stills, crucial for sustainable desalination in water-stressed regions.
2710 L/year
Predicted freshwater yield for Zahedan
Executive Impact
Key metrics demonstrating the tangible benefits of AI in renewable energy and water management.
97%
Prediction Accuracy (R²)
2630 L
Tehran Annual Yield (2024-2033)
Deep Learning
Core Methodology
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The research focuses on modified pyramid solar stills as a sustainable solution for fresh water production. These systems are crucial for remote areas facing water scarcity, leveraging solar energy for desalination.
Key operational factors influencing performance include solar irradiance, ambient temperature, wind speed, and the physical characteristics of the still (surface area, tilt angle).
The study employs advanced deep learning models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and a hybrid CNN-LSTM algorithm.
These models are specifically chosen for their proficiency in time series analysis, enabling accurate long-term predictions of fluctuating environmental data.
Monthly meteorological data from 1984 to 2023 for Tehran and Zahedan, Iran, were used. The methodology involved data collection, preprocessing (normalization, feature selection), model training, and evaluation.
The goal was to predict global solar irradiance (GHI) and temperature (T2M) for the next ten years (2024-2033) to estimate solar still output.
Key Prediction Metric
0.0526 Lowest RMSE for GHI prediction (Tehran, CNN model)Deep Learning Forecasting Process
Meteogorical Data
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Data Collection
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Feature Selection
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Data Normalization
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DNN Models (CNN, LSTM, GRU, CNN-LSTM)
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GHI Prediction
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T2M Prediction
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Best Model Selection
| Model | Location | GHI R² (test) | T2M R² (test) |
|---|---|---|---|
| CNN | Tehran | 0.9671 | 0.9719 |
| LSTM | Zahedan | 0.9595 | 0.9589 |
| GRU | Tehran | 0.9664 | 0.9721 |
| CNN-LSTM | Tehran | 0.9690 | 0.9690 |
Regional Prediction Accuracy
The study revealed significant regional differences in prediction accuracy. Tehran generally exhibited better accuracy across models for both GHI and T2M, attributed to more stable climatic conditions. In contrast, Zahedan's more variable climate posed greater challenges, leading to slightly lower, though still strong, R² values. This highlights the importance of regional calibration for model deployment.
0.9695
Tehran Avg R² (GHI/T2M)
0.9592
Zahedan Avg R² (GHI/T2M)
Freshwater Yield Forecast
2630 L/year Predicted average annual freshwater yield for Tehran (2024-2033)Calculate Your Desalination ROI
Estimate the potential freshwater production and cost savings using AI-driven forecasts for solar stills in your region.
Estimated Annual Savings
$0
Equivalent Water Reclaimed (Liters)
0
Your AI Implementation Roadmap
A clear path to integrating AI-driven forecasting into your renewable energy and water desalination projects.
Phase 1: Data Acquisition & Preprocessing
Gathering historical meteorological data, including solar irradiance and temperature, and applying data normalization techniques to ensure model readiness.
Phase 2: Model Selection & Training
Evaluating and training various deep learning models (LSTM, GRU, CNN, CNN-LSTM) with regional data to identify the best-performing algorithm for your specific location.
Phase 3: Long-term Forecasting & Yield Prediction
Utilizing the optimized models to forecast future solar radiation and temperature, subsequently predicting the long-term freshwater yield of the solar still.
Phase 4: System Integration & Optimization
Integrating the AI forecasts into solar still design and operation, enabling dynamic adjustments to maximize efficiency and freshwater output.
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