Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm
Abstract
:1. Introduction
2. Photovoltaic Power Data Preprocessing
- (1)
- There is a strong correlation between PV power and meteorological data. Missing values are replaced with the mean of values before and after the missing value. If a large amount of data are missing during the day, the data for that day are deleted to prevent human influence. Replacement of missing data can be performed using the following formula:
- (2)
- If there is no significant change in radiance or other meteorological data but the data on photovoltaic power generation have changed significantly, this value needs to be removed. In addition, if the photoelectric energy is negative, then in the case of very low radiation or zero, 0 is used instead of the negative value.
- (3)
- The resolution frame rate of the database data is changed. The data interval needed to predict actual PV power over a short period of time is between 15 min and 1 h. Given the short time span of minute-level database data, the application of the original 1 min resolution data is not common and even less in production practice. The data collected are, therefore, converted into 15 min resolution.
- (4)
- Data normalization is necessary. Because meteorological factors such as solar radiation have different dimensions, directly introducing them into the model reduces the accuracy of power prediction. Normalization of data can speed up model training and improve prediction accuracy. In general, maximum and minimum principles are used in combination for data normalization, and the formula is as follows:
3. Principle of Convolutional Neural Networks
4. Aquila Optimization Algorithm
- (1)
- Expand the exploration is the first stage when the Aquila is hunting birds in the air. The birds use vertical glide height to expand the search scope. Its mathematical formula is:
- (2)
- Downsizing is the second stage when the Aquila flock finds its prey from high in the air. It chooses to spiral over the target, prepares to land, and then attacks. The mathematical expression can be shown as:
- (3)
- To expand the development phase in the third stage, when the Aquila birds are in the hunting area, ready for landing and attack, they generally adopt the vertical drop method. The mathematical formula is:
- (4)
- To reduce the development in this stage when the Aquila bird is close to its prey, there is a certain randomness due to attack on the prey, and walking and capturing the prey. This is expressed in the mathematical formula:
5. AO-CNN Short-Term Photovoltaic Power Prediction Model
6. Photovoltaic Power Prediction Based on the IAO-LSTM Network
6.1. LSTM Neural Network
6.2. Improved Aquila Optimization Algorithm
6.3. The Short-Term Photovoltaic Power Prediction Model of the IAO-LSTM Network
6.4. Model Verification
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Season | Error Type | CNN | AO-CNN |
---|---|---|---|
Spring | RMSE (%) | 2.14 | 1.83 |
MAE (%) | 4.95 | 2.90 | |
MAPE(%) | 1.25 | 3.84 | |
Summer | RMSE (%) | 2.00 | 1.75 |
MAE (%) | 3.07 | 2.68 | |
MAPE(%) | 5.02 | 5.11 | |
Autumn | RMSE (%) | 1.49 | 1.25 |
MAE (%) | 2.76 | 2.24 | |
MAPE(%) | 6.56 | 6.88 | |
Winter | RMSE (%) | 1.20 | 0.89 |
MAE (%) | 2.72 | 1.60 | |
MAPE(%) | 6.91 | 8.39 |
Functions | Expression | Search Space | Dimension | Optimal Solution |
---|---|---|---|---|
F1 | [−200,200] | 30 | 0 | |
F2 | [−2.28,2.28] | 30 | 0 | |
F3 | [−22,22] | 30 | 0 | |
F4 | [−40,40] | 30 | 0 |
Season | Error Type | LSTM | AO-LSTM | IAO-LSTM |
---|---|---|---|---|
Spring | RMSE (%) | 1.87 | 1.48 | 1.38 |
MAE (%) | 2.67 | 1.76 | 1.91 | |
MAPE (%) | 1.32 | 3.84 | 6.56 | |
Summer | RMSE (%) | 1.84 | 1.08 | 0.91 |
MAE (%) | 2.92 | 1.66 | 1.29 | |
MAPE (%) | 4.61 | 6.38 | 6.31 | |
Autumn | RMSE (%) | 0.90 | 0.84 | 0.71 |
MAE (%) | 1.13 | 1.26 | 0.84 | |
MAPE (%) | 7.76 | 6.88 | 7.47 | |
Winter | RMSE (%) | 1.01 | 0.71 | 0.61 |
MAE (%) | 1.43 | 1.15 | 0.91 | |
MAPE (%) | 6.91 | 8.39 | 5.02 |
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Liu, L.; Li, Y. Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm. Processes 2023, 11, 1957. https://doi.org/10.3390/pr11071957
Liu L, Li Y. Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm. Processes. 2023; 11(7):1957. https://doi.org/10.3390/pr11071957
Chicago/Turabian StyleLiu, Liqun, and Yang Li. 2023. "Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm" Processes 11, no. 7: 1957. https://doi.org/10.3390/pr11071957
APA StyleLiu, L., & Li, Y. (2023). Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm. Processes, 11(7), 1957. https://doi.org/10.3390/pr11071957