Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm
Abstract
:1. Introduction
2. Data Processing and Analysis
2.1. Data Processing
2.2. Pearson Correlation Coefficient Analysis
2.3. K-Means Clustering Analysis
- (1)
- The distinction between rainfall weather and other weather conditions can be made based on the quantity of rainfall at different intervals of each day and the occurrence of rain. Herein, the lower threshold is set at 0.5 mm. If the cumulative daily rainfall exceeds 0.5 mm, the weather of that day is classified as a rainy day.
- (2)
- GHI fluctuation: Sunny days usually have a consistently stable irradiance, resulting in stable and high-power output from photovoltaic modules. Conversely, the movement of clouds in cloudy weather leads to rapid changes in irradiance, thereby causing fluctuations in PV power generation. In this study, the standard deviation of irradiance fluctuation is used as a judgment indicator. If the standard deviation of irradiance exceeds 50 W/m2, it is determined as a cloudy day; otherwise, it is determined as a sunny day. The reason for choosing the standard deviation as the evaluation criterion lies in that the standard deviation can effectively measure the degree of dispersion of irradiance values and reflect the magnitude of irradiance fluctuation.
- (1)
- Specify the number of clusters K: Set the number of clusters K for the samples at three, corresponding respectively to the three weather types, namely sunny, cloudy, and rainy. Select three samples from the sample set as the initial cluster centers.
- (2)
- The Euclidean distance from each data point to the center point of k classes is calculated in turn, and all samples are divided into the nearest class according to the principle of the nearest to the center point of K. The Euclidean distance is:
- (3)
- Determines whether the conditions for terminating clustering are met, and if not, returns to step 2. The condition of termination is to meet the following objective function, that is, if the sum of squares of the deviation of each sample to the center point of the class is less than the specified value, and the clustering terminates.
3. GVSAO-Bi-LSTM Algorithm
3.1. Good Point Strategy
3.2. Vibration Strategy
3.3. Snow Ablation Optimization Algorithm
3.4. Bi-LSTM Neural Network Model
3.5. LSTM Neural Network Model
4. Analysis on the Implementation Process and Test Functions of GVSAO
4.1. The Implementation Procedure of GVSAO
4.2. Analysis of Test Functions
- Unimodal function
- Multimodal function
- Combined function
5. Construction of PV Power Prediction Model Based on GVSAO-Bi-LSTM
5.1. The Process of Model Construction
5.2. Case Verification and Analysis
- (1)
- Data normalization and denormalization procedures
- (2)
- Analysis of the prediction outcomes of PV power generation
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Function Number | Function | Dimension | Value Range | Minimum Value |
---|---|---|---|---|
F1 | 30 | [−100, 100] | 0 | |
F2 | 30 | [−32, 32] | 0 | |
F3 | 2 | [−5, 5] | 0.398 |
Function | Evaluation Index | GVSAO | SAO | GWO | PSO | GA |
---|---|---|---|---|---|---|
F1 | avg | 3.28 × 10−92 | 3.12 × 10−80 | 1.31 × 10−27 | 0.51 | 0.0007 |
F2 | avg | 4.44 × 10−16 | 2.86 × 10−15 | 5.93 × 10−14 | 0.95 | 0.057 |
F3 | avg | 0.398093 | 0.397968 | 0.397889 | 0.397887 | 0.396761 |
Weather Type | Model | Error Index | |||
---|---|---|---|---|---|
MSE | RMSE | MAE | MAPE | ||
Clear | BP | 0.4462 | 0.6681 | 0.4813 | 95.81% |
RNN | 0.3169 | 0.5629 | 0.3677 | 78.01% | |
LSTM | 0.2868 | 0.5356 | 0.3395 | 61.06% | |
Bi-LSTM | 0.2322 | 0.4819 | 0.2876 | 36.59% | |
GVSAO-Bi-LSTM | 0.2048 | 0.4526 | 0.2714 | 4.75% | |
Cloudy | BP | 0.4529 | 0.6731 | 0.5152 | 335.20% |
RNN | 0.3384 | 0.5817 | 0.3723 | 147.58% | |
LSTM | 0.2879 | 0.5366 | 0.3449 | 73.16% | |
Bi-LSTM | 0.2392 | 0.4891 | 0.3073 | 48.93% | |
GVSAO-Bi-LSTM | 0.2092 | 0.4574 | 0.2919 | 5.41% | |
Rainy | BP | 0.5607 | 0.7489 | 0.5658 | 338.24% |
RNN | 0.3549 | 0.5957 | 0.3774 | 259.27% | |
LSTM | 0.3517 | 0.5931 | 0.3711 | 141.08% | |
Bi-LSTM | 0.2898 | 0.5383 | 0.3338 | 73.32% | |
GVSAO-Bi-LSTM | 0.2714 | 0.5209 | 0.3241 | 14.37% |
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Wu, Y.; Xiang, C.; Qian, H.; Zhou, P. Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm. Energies 2024, 17, 4434. https://doi.org/10.3390/en17174434
Wu Y, Xiang C, Qian H, Zhou P. Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm. Energies. 2024; 17(17):4434. https://doi.org/10.3390/en17174434
Chicago/Turabian StyleWu, Yuhan, Chun Xiang, Heng Qian, and Peijian Zhou. 2024. "Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm" Energies 17, no. 17: 4434. https://doi.org/10.3390/en17174434
APA StyleWu, Y., Xiang, C., Qian, H., & Zhou, P. (2024). Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm. Energies, 17(17), 4434. https://doi.org/10.3390/en17174434