# Wind Turbine Noise Prediction Using Random Forest Regression

^{*}

## Abstract

**:**

## 1. Introduction

#### Recent Researches on the Subject

## 2. Methodology

- Solo 01 dB integrating sound level meter model of “Class 1”
- Larson Davis CAL 200 Calibrator
- Tripod
- Windproof headphones

- WEC1Ac: Active power (kW) measured at Wind Turbine 1
- WEC1WS: Wind speed (m/s) measured at nacelle of the Wind Turbine 1
- WEC2Ac: Active power (kW) measured at Wind Turbine 2
- WEC2WS: Wind speed (m/s) measured at nacelle of the Wind Turbine 2
- WEC3Ac: Active power (kW) measured at Wind Turbine 3
- WEC3WS: Wind speed (m/s) measured at nacelle of the Wind Turbine 3
- WEC4Ac: Active power (kW) measured at Wind Turbine 4
- WEC4WS: Wind speed (m/s) measured at nacelle of the Wind Turbine 4

## 3. Results

#### 3.1. Acoustic Measurements and SCADA Data Analysis

#### 3.2. Data Processing

#### 3.3. Linear Regression Analysis

#### 3.4. Random Forest Model

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Map view of the wind farm with indication of the towers. All the towers are located at a height higher than that of the receiver. The distances between the towers and the receiver are as follows: 1 = 230 m., 2 = 250 m., 3 = 400 m., 4 = 780 m.

**Figure 2.**Equipment during the acoustic measurement process (on the left) and a view from the window of the house where it is clearly possible to identify one of the 4 turbines (on the right).

**Figure 3.**The algorithm requires each tree of a random forest to be trained using a different random subset of fixed cardinality generated from the set of data available. The forecasts obtained from the trees are collected and averaged, in this way, the result will be closer to the actual value.

**Figure 7.**Variables importance of the Random Forest Model predictors (WEC1Ac—Active power measured at Wind Turbine 1, WEC1WS—Wind speed measured at Wind Turbine 1, WEC2Ac—Active power measured at Wind Turbine 2, WEC2WS—Wind speed measured at Wind Turbine 2, WEC3Ac—Active power measured at Wind Turbine 3, WEC3WS—Wind speed measured at Wind Turbine 3, WEC4Ac—Active power measured at Wind Turbine 4, WEC4WS—Wind speed measured at Wind Turbine 4).

**Figure 8.**Actual versus predicted values for both the tested models (Multiple linear regression model at the left, Random Forest model at the right).

WT 1 | WT 2 | WT 3 | WT 4 | |||||
---|---|---|---|---|---|---|---|---|

Time Stamp | Active Power [kW] | Wind Speed [m/s] | Active Power [kW] | Wind Speed [m/s] | Active Power [kW] | Wind Speed [m/s] | Active Power [kW] | Wind Speed [m/s] |

01:20 | 2058.6 | 15.6 | 2058.1 | 16.1 | 2059.9 | 16.5 | 2058.6 | 16.2 |

01:30 | 2059.5 | 14.9 | 2057.9 | 15.8 | 2058.4 | 15.4 | 2059.2 | 15.3 |

01:40 | 2058.9 | 13.6 | 2054.9 | 14.0 | 2058.4 | 14.2 | 2058.1 | 14.5 |

01:50 | 2012.4 | 11.7 | 2040.5 | 12.4 | 2039.9 | 12.5 | 2049.5 | 12.2 |

02:00 | 1846.9 | 10.7 | 1938.3 | 11.3 | 1992.6 | 11.5 | 2044.4 | 12.0 |

02:10 | 1632.2 | 10.1 | 1800.7 | 10.9 | 1930.9 | 11.2 | 1634.5 | 9.9 |

02:20 | 1957.5 | 11.7 | 1794.3 | 11.0 | 1999.9 | 11.9 | 1854.1 | 10.9 |

02:30 | 1907.6 | 11.6 | 1981.6 | 12.4 | 1964.9 | 12.9 | 1642.1 | 10.7 |

02:40 | 2028.1 | 13.5 | 2050.7 | 14.9 | 2029.2 | 14.6 | 2050.2 | 14.7 |

02:50 | 2052.5 | 16.6 | 2052.8 | 18.6 | 2041.5 | 17.8 | 2053.1 | 17.0 |

03:00 | 2055.3 | 18.9 | 2052.0 | 18.7 | 2042.5 | 19.5 | 2052.2 | 19.6 |

03:10 | 2055.9 | 21.3 | 2048.9 | 21.3 | 2053.5 | 20.5 | 2048.5 | 21.6 |

03:20 | 2058.9 | 22.3 | 2044.1 | 22.4 | 1372.1 | 21.0 | 2052.5 | 22.3 |

WT 1 | WT 2 | WT 3 | WT 4 | |||||
---|---|---|---|---|---|---|---|---|

Time Stamp | Active Power [kW] | Wind Speed [m/s] | Active Power [kW] | Wind Speed [m/s] | Active Power [kW] | Wind Speed [m/s] | Active Power [kW] | Wind Speed [m/s] |

Max | 2059.5 | 22.3 | 2058.1 | 22.4 | 2059.9 | 21 | 2059.2 | 22.3 |

Min | 1632.2 | 10.1 | 1794.3 | 10.9 | 1372.1 | 11.2 | 1634.5 | 9.9 |

Time Stamp | Wind Speed WT1 [m/s] | Wind Speed WT2 [m/s] | Wind Speed WT3 [m/s] | Wind Speed WT4 [m/s] | LeqA [dBA] |
---|---|---|---|---|---|

01:20 | 15.6 | 16.1 | 16.5 | 16.2 | 40.6 |

01:30 | 14.9 | 15.8 | 15.4 | 15.3 | 38.5 |

01:40 | 13.6 | 14.0 | 14.2 | 14.5 | 36.9 |

01:50 | 11.7 | 12.4 | 12.5 | 12.2 | 38.1 |

02:00 | 10.7 | 11.3 | 11.5 | 12.0 | 38.2 |

02:10 | 10.1 | 10.9 | 11.2 | 9.9 | 38.4 |

02:20 | 11.7 | 11.0 | 11.9 | 10.9 | 38.7 |

02:30 | 11.6 | 12.4 | 12.9 | 10.7 | 41.1 |

02:40 | 13.5 | 14.9 | 14.6 | 14.7 | 41.7 |

02:50 | 16.6 | 18.6 | 17.8 | 17.0 | 45.5 |

03:00 | 18.9 | 18.7 | 19.5 | 19.6 | 49.7 |

03:10 | 21.3 | 21.3 | 20.5 | 21.6 | 47.3 |

03:20 | 22.3 | 22.4 | 21.0 | 22.3 | 47.6 |

RMSE | Person’s Correlation Coefficient |
---|---|

0.003788142 | 0.897 |

RMSE | Person’s Correlation Coefficient |
---|---|

0.0007370879 | 0.981 |

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**MDPI and ACS Style**

Iannace, G.; Ciaburro, G.; Trematerra, A.
Wind Turbine Noise Prediction Using Random Forest Regression. *Machines* **2019**, *7*, 69.
https://doi.org/10.3390/machines7040069

**AMA Style**

Iannace G, Ciaburro G, Trematerra A.
Wind Turbine Noise Prediction Using Random Forest Regression. *Machines*. 2019; 7(4):69.
https://doi.org/10.3390/machines7040069

**Chicago/Turabian Style**

Iannace, Gino, Giuseppe Ciaburro, and Amelia Trematerra.
2019. "Wind Turbine Noise Prediction Using Random Forest Regression" *Machines* 7, no. 4: 69.
https://doi.org/10.3390/machines7040069