Neural Network MPPT with NASA POWER Irradiance and Temperature Data
Neural Network MPPT with NASA POWER Irradiance and Temperature Data
𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧
Maximum Power Point Tracking (MPPT) is an essential control technique used in solar photovoltaic systems to extract the maximum available power under changing environmental conditions.
Neural Network MPPT with NASA POWER Irradiance and Temperature Data
This MATLAB/Simulink implementation demonstrates a 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤-𝐛𝐚𝐬𝐞𝐝 𝐌𝐏𝐏𝐓 system trained using historical:
Solar irradiance data
Temperature data
PV panel characteristics
The environmental data is collected from the 𝐍𝐀𝐒𝐀 𝐏𝐎𝐖𝐄𝐑 Data Access Viewer for a selected geographical location.
The trained neural network estimates the required 𝐯𝐨𝐥𝐭𝐚𝐠𝐞 𝐚𝐭 𝐦𝐚𝐱𝐢𝐦𝐮𝐦 𝐩𝐨𝐰𝐞𝐫 𝐩𝐨𝐢𝐧𝐭, which is then used by the converter control system to operate the PV panel close to its maximum available power.
𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰
The complete system combines historical weather data processing, neural network training, PV modeling, and boost converter control.
Main components
NASA POWER historical weather database
Irradiance dataset
Temperature dataset
MATLAB data processing
Neural Network fitting tool
Solar PV panel
Neural Network MPPT controller
PI controller
PWM generator
IGBT-based boost converter
Variable resistive load
Voltage measurement
Current measurement
Power measurement
Overall power flow
𝐏𝐕 𝐏𝐚𝐧𝐞𝐥 → 𝐁𝐨𝐨𝐬𝐭 𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫 → 𝐋𝐨𝐚𝐝
The boost converter switching operation is controlled using the maximum-power-point voltage predicted by the trained neural network.
𝐍𝐀𝐒𝐀 𝐏𝐎𝐖𝐄𝐑 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧
The first stage is the collection of historical environmental data.
The NASA POWER Data Access Viewer is used to obtain weather information for a selected location.
Data collection procedure
Open the NASA POWER website.
Select the POWER Data Access Viewer.
Choose the required geographical location.
Select the required date range.
Choose the environmental parameters.
Select CSV as the export format.
Submit the request.
Download the historical data.
Import the data into MATLAB.
For the demonstrated system, 𝐂𝐡𝐞𝐧𝐧𝐚𝐢 is selected as the geographical location.
Data used for neural network training
Parameter | Description |
Location | Chennai |
Historical period | Up to approximately 6 months |
Solar input | Surface downward irradiance |
Temperature input | Temperature data |
Export format | CSV |
MATLAB irradiance variable | IGG |
MATLAB temperature variable | PT |
Combined dataset | data |
Using several months of environmental information gives the neural network a range of operating conditions for training.
𝐏𝐕 𝐏𝐚𝐧𝐞𝐥 𝐃𝐚𝐭𝐚 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧
PV panel parameters are entered in MATLAB based on the manufacturer's datasheet.
The required parameters include:
PV Parameter | Purpose |
Short-circuit current | Defines PV current characteristics |
Current at maximum power point | Used for MPP calculation |
Open-circuit voltage | Defines maximum unloaded voltage |
Voltage at maximum power point | Target operating information |
Temperature coefficient parameters | Account for temperature variation |
Standard test temperature | Reference operating condition |
Irradiance information | Determines available solar power |
These parameters are combined with the historical irradiance and temperature data to prepare the neural network training dataset.
𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠
MATLAB is used to generate the required 𝐢𝐧𝐩𝐮𝐭 𝐚𝐧𝐝 𝐨𝐮𝐭𝐩𝐮𝐭 𝐝𝐚𝐭𝐚 for neural network training.
Neural network inputs
The network receives:
Irradiance
Temperature
Neural network output
The network predicts:
𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐚𝐭 𝐌𝐚𝐱𝐢𝐦𝐮𝐦 𝐏𝐨𝐰𝐞𝐫 𝐏𝐨𝐢𝐧𝐭
This predicted voltage becomes the reference for the PV MPPT controller.
Training procedure
Load the historical dataset into MATLAB.
Generate neural network input data.
Generate corresponding target output data.
Open the MATLAB neural network fitting tool.
Select the input matrix.
Select the output matrix.
Configure training, validation, and testing.
Start neural network training.
Check the regression performance.
Export the trained network for Simulink.
In the demonstrated training process, the regression result reaches approximately:
Dataset | Regression Result |
Training | R ≈ 1 |
Validation | R ≈ 1 |
Testing | R ≈ 1 |
Overall | R ≈ 1 |
An R value close to 1 indicates a very strong relationship between the network predictions and the target training data.
𝐒𝐢𝐦𝐮𝐥𝐢𝐧𝐤 𝐌𝐨𝐝𝐞𝐥 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞
After training, the neural network is exported to Simulink and integrated into the PV MPPT system.
The model consists of:
PV source
Irradiance input
Temperature input
Neural network
PI controller
PWM generator
Boost converter
Resistive load
Measurement blocks
Scope blocks
The PV array receives continuously changing environmental conditions while the neural network calculates the appropriate reference voltage.
𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲
The control system follows a straightforward sequence.
Step 1 – Environmental inputs
The PV system receives:
Solar irradiance
Temperature
Step 2 – Neural network prediction
The trained neural network uses the environmental conditions to predict the expected 𝐌𝐏𝐏 𝐯𝐨𝐥𝐭𝐚𝐠𝐞.
Step 3 – Voltage control
The predicted maximum-power-point voltage is used as the reference for the controller.
The measured PV voltage is compared with this desired operating point.
Step 4 – PI controller
The PI controller processes the voltage control signal and produces the required converter control command.
Step 5 – PWM generation
The PWM generator converts the controller output into switching pulses.
Step 6 – IGBT switching
The PWM pulses control the IGBT in the boost converter.
Step 7 – Maximum power extraction
By continuously adjusting the boost converter operating condition, the PV system tracks the maximum available solar power.
𝐁𝐨𝐨𝐬𝐭 𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫
The boost converter forms the interface between the PV source and load.
Its main components include:
Input capacitor
Inductor
IGBT switch
Diode
Output capacitor
Load
The neural network does not directly switch the IGBT. Instead, the predicted MPP voltage is processed by the control system before generating the PWM signal.
This arrangement allows the converter to continuously adjust the electrical operating point of the PV panel.
𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐧𝐜𝐞 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭
The trained MPPT controller is tested under several solar irradiance conditions.
The applied irradiance levels are:
Test Condition | Irradiance |
Condition 1 | 1000 W/m² |
Condition 2 | 800 W/m² |
Condition 3 | 600 W/m² |
Condition 4 | 400 W/m² |
Condition 5 | 200 W/m² |
A load resistance of approximately 𝐑 = 𝟏𝟓.𝟕𝟖 Ω is used during the irradiance variation test.
𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬
The simulated PV output power is compared with the available maximum power of the panel.
Irradiance | Available Maximum Power | Extracted Power |
1000 W/m² | 213.1 W | 212.8 W |
800 W/m² | 171.8 W | 171.7 W |
600 W/m² | 129.5 W | 129.4 W |
400 W/m² | 86.26 W | 86.21 W |
200 W/m² | 42.46 W | 39.32 W |
The results show that the neural network controller maintains PV operation very close to the available maximum power over most of the tested irradiance range.
𝐌𝐏𝐏𝐓 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
The comparison between available and extracted PV power demonstrates the effectiveness of the trained controller.
At 1000 W/m²
Maximum available power: 213.1 W
Extracted power: 212.8 W
Operation is extremely close to the maximum power point.
At 800 W/m²
Maximum available power: 171.8 W
Extracted power: 171.7 W
Very small difference between theoretical and extracted power.
At 600 W/m²
Maximum available power: 129.5 W
Extracted power: 129.4 W
Neural network maintains accurate MPPT operation.
At 400 W/m²
Maximum available power: 86.26 W
Extracted power: 86.21 W
The controller continues tracking effectively at lower irradiance.
At 200 W/m²
Maximum available power: 42.46 W
Extracted power: 39.32 W
Some deviation appears under the lowest irradiance condition, but useful power tracking is maintained.
𝐋𝐨𝐚𝐝 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐬𝐭
The Neural Network MPPT system is also evaluated under changing load conditions.
For this test:
Parameter | Setting |
Irradiance | 1000 W/m² |
Load type | Resistive |
Load variation interval | Every 0.3 s |
Example load values | Approximately 13 Ω, 15 Ω and other values |
Different resistive loads are connected at different time intervals.
Despite these load variations, the controller adjusts the boost converter operation and continues extracting power close to the PV maximum power point.
𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬
The entire Neural Network MPPT implementation can be summarized as:
Select the geographical location.
Collect NASA POWER irradiance data.
Collect NASA POWER temperature data.
Export the historical data as CSV.
Import the dataset into MATLAB.
Enter PV panel datasheet parameters.
Generate neural network training inputs and targets.
Train the neural network.
Verify regression performance.
Export the network to Simulink.
Use the neural network to predict MPP voltage.
Process the reference through a PI controller.
Generate PWM switching pulses.
Control the boost converter IGBT.
Operate the PV panel near its maximum power point.
Test the controller under irradiance variations.
Test the controller under load variations.
𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬
𝐍𝐀𝐒𝐀 𝐏𝐎𝐖𝐄𝐑 historical data integration
Real environmental irradiance data
Real environmental temperature data
MATLAB-based training dataset generation
Neural network MPPT implementation
Maximum-power-point voltage prediction
MATLAB Neural Network fitting
Simulink integration of the trained network
PI-based converter control
PWM-controlled boost converter
Testing under multiple irradiance levels
Testing under variable load conditions
PV voltage, current, and power monitoring
Suitable for understanding intelligent solar MPPT control
𝐖𝐡𝐲 𝐔𝐬𝐞 𝐍𝐀𝐒𝐀 𝐏𝐎𝐖𝐄𝐑 𝐃𝐚𝐭𝐚?
Using historical environmental data makes the neural network training process more representative of changing weather conditions.
Instead of relying on only one irradiance and temperature point, the network can be trained using several months of recorded conditions.
This approach helps demonstrate how 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐌𝐏𝐏𝐓 can be integrated with conventional power electronic converters.
𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬 𝐨𝐟 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐌𝐏𝐏𝐓
Learns the relationship between environmental conditions and PV operating point
Can predict the desired MPP voltage rapidly
Suitable for nonlinear PV characteristics
Can operate under changing irradiance
Can accommodate temperature variations
Works with standard DC–DC converter structures
Reduces dependence on continuous perturbation of the operating point
Can be trained using location-specific historical weather information
Easily integrated into MATLAB/Simulink
𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
This Neural Network MPPT methodology can be studied for applications such as:
Solar PV energy conversion systems
Standalone PV systems
Grid-connected PV systems
Battery-integrated PV systems
DC microgrids
Renewable energy conversion
Solar charging systems
Intelligent DC–DC converter control
Machine learning-based energy control
Data-driven renewable energy systems
𝐖𝐡𝐨 𝐂𝐚𝐧 𝐔𝐬𝐞 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥?
The implementation is useful for:
Electrical engineering students
Power electronics learners
Renewable energy researchers
MATLAB/Simulink users
Solar PV engineers
Control system researchers
Machine learning researchers working on renewable energy
Engineers studying intelligent MPPT techniques
𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐁𝐞 𝐋𝐞𝐚𝐫𝐧𝐞𝐝?
By studying this implementation, users can understand:
How to obtain historical NASA POWER weather data
How to import irradiance and temperature data into MATLAB
How to prepare data for neural network training
How PV datasheet parameters are used
How to train a fitting neural network
How to interpret neural network regression results
How to export a trained network to Simulink
How Neural Network MPPT works
How a PI controller regulates PV voltage
How PWM controls a boost converter
How MPPT behaves under irradiance variation
How MPPT responds to load variation
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧
The 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐌𝐏𝐏𝐓 𝐢𝐧 𝐌𝐀𝐓𝐋𝐀𝐁/𝐒𝐢𝐦𝐮𝐥𝐢𝐧𝐤 demonstrates a data-driven approach for maximum power extraction from a solar PV system.
Historical 𝐍𝐀𝐒𝐀 𝐏𝐎𝐖𝐄𝐑 irradiance and temperature data are used to prepare the neural network training dataset. Once trained, the network predicts the voltage corresponding to the maximum power operating point.
The predicted reference is combined with a PI controller, PWM generator, and boost converter to regulate PV operation.
Simulation results show close tracking of the available maximum PV power across irradiance levels from 1000 W/m² down to 200 W/m². The controller also maintains maximum-power operation when the connected load changes.
Overall, this implementation provides a clear example of combining 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐬𝐨𝐥𝐚𝐫 𝐏𝐕, 𝐌𝐏𝐏𝐓, and 𝐩𝐨𝐰𝐞𝐫 𝐞𝐥𝐞𝐜𝐭𝐫𝐨𝐧𝐢𝐜𝐬 within MATLAB/Simulink.
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