# Day-Ahead Electricity Price Probabilistic Forecasting Based on SHAP Feature Selection and LSTNet Quantile Regression

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. SHapley Additive exPlanation-SHAP

#### 2.2. Long- and Short-Term Time-Series Network-LSTNet

- (1)
- Convolutional module

- (2)
- Recurrent and Recurrent-skip modules

- (3)
- Autoregressive module

#### 2.3. LSTNet Quantile Regression

#### 2.3.1. Linear Quantile Regression

#### 2.3.2. Neural Network Quantile Regression

#### 2.3.3. LSTNet Quantile Regression

#### 2.4. Evaluation Metrics

## 3. Case Studies

#### 3.1. Overview of the Danish Electricity Market

#### 3.2. Feature Selection and Analysis

#### 3.3. Probabilistic Forecasting Results

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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Feature Name | Explanation |
---|---|

DK1_7 | DK1 electricity price seven days lag |

DK1_2 | DK1 electricity price two days lag |

DK1_1 | DK1 electricity price one day lag |

DK2_7 | DK2 electricity price seven days lag |

DK2_2 | DK2 electricity price two days lag |

DK2_1 | DK2 electricity price one day lag |

DE_1 | DE electricity price one day lag |

NO2_1 | NO2 electricity price one day lag |

SE3_1 | SE3 electricity price one day lag |

SE4_1 | SE4 electricity price one day lag |

SYS_1 | system price one day lag |

Pro_1 | DK1 production one day lag |

Con_1 | DK1 consumption one day lag |

Wind_1 | DK1 wind power one day lag |

Solar_1 | DK1 solar power one day lag |

HydroPower_1 | DK1 hydropower one day lag |

ExNO_1 | electricity exchange between DK1 and NO |

ExGE_1 | electricity exchange between DK1 and DE |

ExGB_1 | electricity exchange between DK1 and DK2 |

RMSE | MAPE | |
---|---|---|

BPNN | 8.35 | 13.02 |

LSTM | 7.8 | 14.62 |

LSTNet | 4.96 | 8.39 |

SHAP–BPNN | 4.27 | 10.34 |

SHAP–LSTM | 3.62 | 9.28 |

SHAP–LSTNet | 2.35 | 5.39 |

0.9 | 0.8 | 0.7 | ||
---|---|---|---|---|

SHAP–BPNN | PINAW | 22.22 | 15.28 | 9.72 |

PICP | 10.87 | 7.42 | 5.77 | |

AL | 2.61 | 2.25 | 4.88 | |

SHAP–LSTM | PINAW | 72.22 | 44.44 | 34.72 |

PICP | 32.74 | 25.72 | 17.42 | |

AL | 0.88 | 1.58 | 1.85 | |

SHAP–LSTNet | PINAW | 97.22 | 80.56 | 77.78 |

PICP | 35.79 | 27.13 | 24.13 | |

AL | 0.41 | 0.80 | 1.14 |

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## Share and Cite

**MDPI and ACS Style**

Liu, H.; Shen, X.; Tang, X.; Liu, J.
Day-Ahead Electricity Price Probabilistic Forecasting Based on SHAP Feature Selection and LSTNet Quantile Regression. *Energies* **2023**, *16*, 5152.
https://doi.org/10.3390/en16135152

**AMA Style**

Liu H, Shen X, Tang X, Liu J.
Day-Ahead Electricity Price Probabilistic Forecasting Based on SHAP Feature Selection and LSTNet Quantile Regression. *Energies*. 2023; 16(13):5152.
https://doi.org/10.3390/en16135152

**Chicago/Turabian Style**

Liu, Huixin, Xiaodong Shen, Xisheng Tang, and Junyong Liu.
2023. "Day-Ahead Electricity Price Probabilistic Forecasting Based on SHAP Feature Selection and LSTNet Quantile Regression" *Energies* 16, no. 13: 5152.
https://doi.org/10.3390/en16135152