# A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model

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## Abstract

**:**

## 1. Introduction

## 2. Forecast Model Framework

## 3. Methodology

#### 3.1. Filter Fuzzy Clustering Variables

#### 3.2. Fuzzy Division of Input Variables

#### 3.3. Establish A DBN Model for Each Fuzzy Subset

#### 3.4. T-S Model Output

## 4. Case Studies

#### 4.1. Data Set Description

#### 4.2. Evaluation Index

#### 4.3. Experimental Setup

#### 4.4. Experimental Results

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**Forecasting results in different seasons. (

**a**) one day in March in spring; (

**b**) one day in June in summer; (

**c**) one day in September in autumn; (

**d**) one day in December in winter.

Insolation | Temperature | Humidity | Wind Speed | Wind Direction | Historical Power |
---|---|---|---|---|---|

0.8279 | 0.5406 | 0.4083 | 0.0117 | 0.0227 | 0.8066 |

Number of RBM Layer | Number of Neurons in Each Layer | MAE/KW | ||||
---|---|---|---|---|---|---|

1 | 2 | 3 | 4 | 5 | ||

2 | 150 | 200 | - | - | - | 14.42 |

200 | 300 | - | - | - | 15.01 | |

250 | 400 | - | - | - | 15.47 | |

300 | 500 | - | - | - | 15.78 | |

3 | 150 | 200 | 250 | - | - | 14.68 |

200 | 300 | 400 | - | - | 14.01 | |

250 | 400 | 550 | - | - | 14.47 | |

300 | 500 | 700 | - | - | 14.89 | |

4 | 150 | 200 | 250 | 300 | - | 14.68 |

200 | 300 | 400 | 500 | - | 15.32 | |

250 | 400 | 550 | 700 | - | 15.75 | |

300 | 500 | 700 | 900 | - | 16.06 | |

5 | 150 | 200 | 250 | 300 | 350 | 14.14 |

200 | 300 | 400 | 500 | 600 | 14.69 | |

250 | 400 | 550 | 700 | 850 | 15.11 | |

300 | 500 | 700 | 900 | 1100 | 15.51 |

Spring | Summer | Autumn | Winter | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

MAE/KW | MRE | RMSE | MAE/KW | MRE | RMSE | MAE/KW | MRE | RMSE | MAE/KW | MRE | RMSE | |

T-S | 7.8596 | 0.0390 | 0.0684 | 6.1446 | 0.0300 | 0.0508 | 10.5962 | 0.0638 | 0.1174 | 9.9633 | 0.0603 | 0.0912 |

DBN | 9.5807 | 0.0761 | 0.1207 | 11.9857 | 0.0809 | 0.1083 | 8.5044 | 0.0752 | 0.1189 | 8.9548 | 0.0805 | 0.1580 |

The proposed model | 4.0440 | 0.0319 | 0.0577 | 5.3429 | 0.0258 | 0.0431 | 7.7214 | 0.0573 | 0.1051 | 6.7769 | 0.0595 | 0.0826 |

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**MDPI and ACS Style**

Liu, L.; Liu, F.; Zheng, Y.
A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model. *Energies* **2021**, *14*, 6447.
https://doi.org/10.3390/en14206447

**AMA Style**

Liu L, Liu F, Zheng Y.
A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model. *Energies*. 2021; 14(20):6447.
https://doi.org/10.3390/en14206447

**Chicago/Turabian Style**

Liu, Ling, Fang Liu, and Yuling Zheng.
2021. "A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model" *Energies* 14, no. 20: 6447.
https://doi.org/10.3390/en14206447