Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting
Abstract
:1. Introduction
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- Identify gaps in existing solar power forecasting models, especially in terms of their capability to take into consideration localized solar irradiance variations and the impact of clear sky conditions.
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- Optimize temporal data handling for better forecasting accuracy, especially in areas with high variability in solar radiation.
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- Introduce an innovative approach to significantly enhance forecasting accuracy by using clear sky solar radiation data, replacing traditional temporal inputs such as time of day.
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- Develop a methodology that can be adapted to other geographical locations, offering a flexible solution that can improve solar power forecasting globally.
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- Validate the proposed model against actual solar power output data, and provide a robust comparison that demonstrates the model’s effectiveness.
2. Data and Method
2.1. Dataset
2.2. Solar Radiation
2.3. Clear Sky Solar Radiation
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- The Ineichen model, which estimates clear sky radiation based on atmospheric parameters such as the water vapor content, ozone concentration, and aerosol optical depth. This model considers the position of the sun, geographical location, and time of year to calculate the clear sky radiation [37,38].
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- The Haurwitz model, which is another approach used to calculate clear sky solar radiation. It utilizes the solar zenith angle, latitude, and time of year to estimate the radiation. This model considers the position of the sun in relation to the site and accounts for the Earth’s curvature. According to one report [39] on clear sky solar radiation models, the Haurwitz model demonstrates superior performance among the models that solely rely on the zenith angle.
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- The simplified Solis model, which is employed for estimating clear sky radiation. This model takes into account the solar zenith angle and the site’s latitude to calculate the clear sky radiation. The model’s accuracy has been reported as 15, 20, and 18 W/m2 for the components of GHI and direct normal irradiance (DNI) [40].
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- The Bird Clear Sky Model, which combines the Bird solar radiation model with clear sky conditions to estimate the Clear Sky GHI. It considers variables such as atmospheric water vapor concentration, ozone concentration, and spectral depth of airborne particles [35].
2.4. Long Short-Term Memory Networks (LSTM) Method
- The input to the sigmoid network layer of each gate consists of the previous step’s internal output state, and the current step’s input value, .
- The output of the sigmoid network layer for each gate undergoes a multiplication operation with a different piece of information to obtain the final output result, specifically:
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- Forget Gate: The output of the sigmoid function is multiplied by the previous step’s memory cell value, .
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- Input Gate: The output of the sigmoid function is multiplied by the value (which is obtained from a function with inputs and ).
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- The value of the memory cell, is updated by adding the output values of the forget gate and the input gate.
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- Output Gate: The output of the sigmoid function, , is multiplied by the value of the memory cell (after being processed by the function). The final result obtained, , represents the output state of the current step.
- : input value of time step t
- : output value of time step t
- : output of the forget gate
- : weight function of forget gate
- : output of the input gate
- : weight function of the input gate
- : vector of new candidate values for time step t
- : weight function of tanh layer to calculate
- : cell state of time step t, t − 1
- : output of the sigmoid layer
- : weight function of the sigmoid layer
3. Simulation
3.1. Simulation Setup
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- Location of solar power plant: The solar power plant is located in a southern province of Vietnam. This specific location was chosen due to high solar irradiance and the availability of data.
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- Meteorological parameters:
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- Data source: The study used Global Horizontal Irradiance (GHI), temperature, wind speed, and humidity data obtained from ground-based measurements.
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- Measurement period: The data covers a period from May 2019 to the end of May 2020, ensuring comprehensive coverage of various weather conditions.
3.2. Data Preprocessing
3.2.1. Feature Extraction
3.2.2. Data Cleaning
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- Outlier detection using the Interquartile Range (IQR) technique, combined with the incorporation of new features (the ratio P/GHI, where P is Output Power and GHI is Global Horizontal Irradiance), and the GHI clustering technique to enhance the effectiveness of anomaly detection.
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- Utilizing the Q2 (median) to replace abnormal or missing values.
3.2.3. Feature Transformation
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- Relevance and direct impact: Clear sky irradiance directly measures the potential solar energy available at a specific location and time, making it a more relevant predictor for solar power output. Unlike traditional time-related parameters, which are indirect indicators and may not capture local environmental variations, clear sky irradiance provides a direct assessment of solar potential.
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- Reducing model complexity: By using clear sky irradiance, we reduce the complexity of the model. Time-related parameters often require additional processing to capture cyclical patterns effectively (e.g., hour of the day and day of the year). In contrast, clear sky irradiance can be used as a continuous variable that inherently includes time-of-day and seasonal effects on solar radiation, streamlining the model architecture.
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- Enhancing model performance: Our empirical tests showed that models using clear sky irradiance as an input consistently outperformed those using traditional time-related parameters. Specifically, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were significantly lower, indicating more accurate predictions.
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- Generalizability: Clear sky irradiance models, such as the Ineichen/Perez model used in our study, are adaptable to different geographic locations without substantial recalibration. This makes the approach more scalable and applicable in diverse settings compared to models heavily reliant on specific time patterns that may vary greatly across regions.
3.3. Train Set and Test Set
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- Training set: Data spanning from May 2019 to April 2020 is utilized to train the forecasting models.
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- Test set: Data from May 2020 is employed to compute the forecasted output capacity and compare it against the measured output, thereby assessing the efficacy of the forecasting model.
3.4. Evaluation of Error
3.5. Experiments Setup
3.6. Comparision Results
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- Model 1: MAPE reduces from 3.491% to 3.08% which is 12% better than the Basic Model’s performance. The RMSE value obtained is lower by about 0.879 MW, an improvement of 29%, reflecting that the large-value errors of Model 1 were less than those in the Basic Model.
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- Model 2: MAPE reduces from 3.491% to 2.766% which is 24% better than the Basic Model’s performance. The RMSE value obtained is lower by about 0.991 MW, an improvement of 45%, reflecting that the large-value errors of Model 2 were less than those in the Basic Model and in Model 1.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Temporal Labels | Meaning | Use |
---|---|---|
Day of the year | Reflects the order of days within a year, ranging from 1 to 365 | Day 1/1 is typically labeled as 1, while day 31/12 is usually labeled as 365 |
Hour of the day | Determines values from 1 to 24 h within a day | Can be labeled from 0 to 23 or 1 to 24 |
Minute of the hour | Determines values from 1 to 60. Depending on the data resolution, it can be labeled accurately by minutes or grouped into intervals | For data with a resolution of 15 min, it can be labeled 0, 15, 30, 45, or labeled as 0, 1, 2, 3 |
Index | GHI | TEMP | WIS | HUM | Output |
---|---|---|---|---|---|
count | 114,336 | 114,336 | 114,336 | 114,336 | 114,336 |
mean | 222.73 | 29.21 | 1.45 | 73.94 | 12.34 |
std | 310.49 | 4.00 | 1.18 | 16.14 | 16.65 |
min | 0 | 19.41 | 0 | 25.97 | 0 |
25% | 0 | 26.1 | 0.33 | 59.22 | 0 |
50% | 0.26 | 28.11 | 1.25 | 78.39 | 0.13 |
75% | 424.49 | 32.41 | 2.16 | 87.95 | 24.75 |
max | 1263.53 | 41.02 | 11.77 | 100 | 48 |
Model Name | _Basic Model [1] | Model_1 | Model_2 |
---|---|---|---|
Data | The dataset consists of 13 months of solar power plant data. The first 12 months are utilized for model training, while the final month is kept for testing. | The same dataset as used for the Basic Model. | The same dataset as used for the Basic Model |
Input and Output | The input data comprise calendar data (day, hour, minute) describing time t and meteorological data (GHI, TEMP, WIS, HUM) at time t, while Outputs are the data points at t + 5 min. | The same features for training and testing as in study [1] were used for the Basic Model. | The inputs are Clearsky GHI at time t and Meteorological Data (GHI, TEMP, WIS, HUM) at time t, while Outputs are the data points at t + 5 min. |
LSTM network configuration | 4-layer LSTM network with 100 hidden nodes per layer (4L 100N). | 4-layer LSTM network with 100 hidden nodes per layer (4L 100N). | 4-layer LSTM network with 100 hidden nodes per layer (4L 100N). |
Training | Training using MAE (Mean Absolute Error) loss function, Rectified Linear Unit (ReLU) activation function, Adaptive Moment Estimation (Adam) optimizers, 50 epochs. | Same as the Basic Model but used Early Stopping technique to reduce training time. | Same as the Basic Model but used Early Stopping technique to reduce training time. |
Training | No Validation | Validation 10% | Validation 10% |
Evaluation Indicator | MSE, RMSE, MAE, MAPE | MSE, RMSE, MAE, MAPE | MSE, RMSE, MAE, MAPE |
Error Type | MAE | MAPE | MSE | RMSE |
---|---|---|---|---|
Measurement Unit | MW | % | MW2 | MW |
Basic Model | 1.676 | 3.491 | 9.499 | 3.082 |
Model 1 | 1.478 | 3.08 | 4.853 | 2.203 |
Model 2 | 1.328 | 2.766 | 4.371 | 2.091 |
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Share and Cite
Bui Duy, L.; Nguyen Quang, N.; Doan Van, B.; Riva Sanseverino, E.; Tran Thi Tu, Q.; Le Thi Thuy, H.; Le Quang, S.; Le Cong, T.; Cu Thi Thanh, H. Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting. Energies 2024, 17, 4174. https://doi.org/10.3390/en17164174
Bui Duy L, Nguyen Quang N, Doan Van B, Riva Sanseverino E, Tran Thi Tu Q, Le Thi Thuy H, Le Quang S, Le Cong T, Cu Thi Thanh H. Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting. Energies. 2024; 17(16):4174. https://doi.org/10.3390/en17164174
Chicago/Turabian StyleBui Duy, Linh, Ninh Nguyen Quang, Binh Doan Van, Eleonora Riva Sanseverino, Quynh Tran Thi Tu, Hang Le Thi Thuy, Sang Le Quang, Thinh Le Cong, and Huyen Cu Thi Thanh. 2024. "Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting" Energies 17, no. 16: 4174. https://doi.org/10.3390/en17164174