# A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection

^{*}

## Abstract

**:**

## 1. Introduction

- An effective trend feature extraction method is developed to extract the trend feature of PV power;
- Trend feature, meteorological data and historical PV power data are used to select the optimal input feature by a FCBF algorithm;
- A BiLSTM model is adopted to predict PV power with high accuracy;
- The proposed model is compared with different PV forecasting models.

## 2. Framework of the Proposed Methodology

- Standard normalization and data procession of the original load data are required before performing trend feature extraction.
- The processed data are decomposed into multiple IMFs by VMD to extract the trend feature that can reflect the short-term effect of PV power.
- The optimal feature-sets of trend feature and the original data are selected by FCBF, and are then integrated as a new input-matrix.
- Finally, the optimal input-set is used in the standard BiLSTM model with a 1-D CNN layer to forecast the PV power.

## 3. Methodology

#### 3.1. Variational Mode Decomposition (VMD)

#### 3.2. Fast Correlation-Based Filter (FCBF)

- Delete the feature that is less relevant to the target variable. Take the i-th feature (${v}_{i}$) in the original data as variable $X$ and the target variable $Y$ as category C. Calculate the $SU({v}_{i},Y)$ of each input feature and $Y$. If $SU({v}_{i},Y)<\xi $ ($\xi $ is threshold), delete the variable ${v}_{i}$, and put the retained feature variables into the set G with an empty initial state.
- Analyze redundancy between feature variables. The feature variables in set G are arranged according to the correlation degree of $SU({v}_{i},Y)$ from large to small. Take the feature variable ${v}_{i}$ with the largest correlation and put it into set Q with empty initial state, and then calculate the $SU({v}_{i},{v}_{j})$ between the remaining characteristic variables ${v}_{j}$ in set G and ${v}_{i}$. If $SU({v}_{i},{v}_{j})>SU({v}_{i},Y)$, remove the variable ${v}_{j}$ from set G.
- Return to step 2 and repeat the operation to finally obtain the optimized input feature set Q.

#### 3.3. Improved BiLSTM Short-Term PV Power Forecasting Model

## 4. Results and Discussion

#### 4.1. Data-Set

#### 4.2. Evaluation Criteria

#### 4.3. Simulation Analysis

#### 4.3.1. Comparison of Different Trend Feature Extraction Models

#### 4.3.2. Selection of the Models’ Input-Set

#### 4.3.3. Comparison and Analysis of Prediction Results

## 5. Conclusions

- The collection process of PV power is complex, and the problem of outliers cannot be avoided. Therefore, the extraction of the trend feature with the VMD algorithm can reduce the prediction error caused by outliers and help the prediction model to fully learn the long-term time characteristics and short-term effects on PV power in this region.
- Reducing the error caused by high dimension of input variables using the FCBF algorithm to extract the optimal input feature set of the prediction model is conducive to improving the performance and efficiency of the prediction model.
- Compared with the commonly used short-term PV power forecasting model, the improved BiLSTM forecasting model can selectively combine historical information and trend information for PV power forecasting because of its unique structure.

- (1)
- Symmetrical loss function is used in this model, and the influence of asymmetric loss function on the prediction effect of the model is not considered. Next, we will conduct in-depth research in this direction.
- (2)
- An FCBF algorithm is used to extract the best feature set of PV prediction. In the FCBF algorithm, it is necessary to set the threshold of symmetrical uncertainty (SU) to extract features. A trial-and-error method was used to select the appropriate threshold in this paper. Next, we will try to use an optimization algorithm combined with the FCBF algorithm to select the optimal feature-set effectively.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## References

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Algorithm | SU | Operation Time(s) |
---|---|---|

EMD | 0.71 | 180 s |

EEMD | 0.72 | 30 s |

VMD | 0.73 | 6 s |

Number | Input Variable |
---|---|

0 | d-1 PV Power |

1 | Weather Relative Humidity |

2 | Global Horizontal Radiation |

3 | Weather Daily Rainfall |

4 | Radiation Global Tilted |

5 | Trend feature |

Model | MAE (kW) | RMSE (kW) | MAPE (%) | |||
---|---|---|---|---|---|---|

1-Day Ahead | 3-Day Ahead | 1-Day Ahead | 3-Day Ahead | 1-Day Ahead | 3-Day Ahead | |

Improved BiLSTM | 0.13 | 0.16 | 0.21 | 0.25 | 13.84 | 16.33 |

Improved BiLSTM ’ | 0.14 | 0.18 | 0.23 | 0.33 | 17.08 | 21.00 |

Improved BiLSTM * | 0.15 | 0.19 | 0.28 | 0.35 | 14.07 | 18.69 |

BiLSTM | 0.19 | 0.21 | 0.30 | 0.31 | 21.17 | 27.82 |

LSTM | 0.21 | 0.28 | 0.30 | 0.38 | 33.84 | 37.71 |

GRU | 0.20 | 0.26 | 0.33 | 0.36 | 27.82 | 42.8 |

ELM | 0.26 | 0.26 | 0.38 | 0.36 | 59.9 | 39.24 |

Model | MAE (kW) | RMSE (kW) | MAPE (%) | |||
---|---|---|---|---|---|---|

1-Day Ahead | 3-Day Ahead | 1-Day Ahead | 3-Day Ahead | 1-Day Ahead | 3-Day Ahead | |

Improved BiLSTM | 0.08 | 0.07 | 0.13 | 0.12 | 5.21 | 13.28 |

Improved BiLSTM ’ | 0.22 | 0.22 | 0.34 | 0.33 | 28.9 | 20.26 |

Improved BiLSTM * | 0.25 | 0.19 | 0.43 | 0.34 | 27.26 | 23.89 |

BiLSTM | 0.13 | 0.16 | 0.23 | 0.26 | 24.00 | 45.48 |

LSTM | 0.16 | 0.20 | 0.28 | 0.30 | 30.36 | 103.55 |

GRU | 0.15 | 0.17 | 0.23 | 0.27 | 29.10 | 87.73 |

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**MDPI and ACS Style**

Wu, K.; Peng, X.; Li, Z.; Cui, W.; Yuan, H.; Lai, C.S.; Lai, L.L. A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection. *Energies* **2022**, *15*, 5410.
https://doi.org/10.3390/en15155410

**AMA Style**

Wu K, Peng X, Li Z, Cui W, Yuan H, Lai CS, Lai LL. A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection. *Energies*. 2022; 15(15):5410.
https://doi.org/10.3390/en15155410

**Chicago/Turabian Style**

Wu, Kaitong, Xiangang Peng, Zilu Li, Wenbo Cui, Haoliang Yuan, Chun Sing Lai, and Loi Lei Lai. 2022. "A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection" *Energies* 15, no. 15: 5410.
https://doi.org/10.3390/en15155410