# A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction

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## Abstract

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^{2}of 0.9929. CNN has the fastest speed with an average computational time of 0.0741s. The hybrid model that mines the spatial feature based on the extracted temporal feature has a better performance than the model mines the temporal feature based on the extracted spatial feature.

## 1. Introduction

- The inherent intermittency and uncertainty of wind power lead to difficulties in accurate and rapid wind power output forecasting.
- Few research has paid attention to the bidirectional learning feature of Bi-LSTM in the application of wind power forecasting while more research has been focused on LSTM by far.
- To our best knowledge, the hybrid model BiLSTM-CNN has not yet been applied in the application of wind power forecasting along with the two-way time feature learning and spatial feature extraction analysis.
- Comparison and evaluation among various deep learning models (CNN, LSTM, Bi-LSTM) and their hybrid models in wind power forecasting area have not been systematically researched.

- Through grey correlation analysis under two different normalization methods, multiple wind speed time series data with different heights are selected as inputs of the proposed model. Through this step, the calculation complexity and time are reduced.
- The BiLSTM-CNN algorithm is innovatively proposed in this research, which can extract time and space features in succession to fully mine the information among the input data and obtain high prediction accuracy. The contribution of this proposed model fills in the research gaps. With the experiment conducted in a real wind farm as a case study, the performance of the proposed model is verified by comparison with other single and hybrid deep learning models.
- Model comparison among different deep learning models (LSTM, BiLSTM, CNN, BiLSTM-CNN, LSTM-CNN, CNN-BiLSTM, CNN-LSTM) are systematically studied in wind power forecasting. Three sets of comparison are conducted. Specifically, the role of the introduction of CNN to extract the spatial features among multiple wind speed series with different height is studied; the comparison between Bi-LSTM and LSTM is also studied to verify the significance bidirectional temporal feature extraction ability of Bi-LSTM; Besides, the comparison of BiLSTM-CNN vs. CNN-BiLSTM and LSTM-CNN vs. CNN-LSTM is also experimented to study the preference between ‘‘the temporal characteristics of time series are extracted in the beginning and later the spatial characteristics are extracted’ and ‘the spatial characteristics of time series are extracted in the beginning and later the temporal characteristics are extracted’.

## 2. Methodology

#### 2.1. Grey Correlation Analysis

**Step 1:**Setting of the reference sequence ${X}_{0}=({\mathrm{X}}_{01},{\mathrm{X}}_{02},\cdots ,{\mathrm{X}}_{0N})$, the at the Wind power generation. Assuming there are m series of factors and the ${i}_{th}$ series can be denoted as ${X}_{i}=({\mathrm{X}}_{i1},{\mathrm{X}}_{i2},\cdots ,{\mathrm{X}}_{iN}),\mathrm{i}=1,2,\cdots ,\mathrm{n}$, The data sequence is shown in Formula (1).

**Step 2:**The reference and comparison sequences are then standardized. There are two normalization methods adopted, namely, average normalization and difference normalization, to transform the matrix into the following matrix and transforms the matrix into the following matrix.

**Step 3:**The absolute value difference between the corresponding element in the reference sequence and the comparison sequence is calculated.

**Step 4:**Calculate the discrete coefficient, which can show the degree of correlation:

**Step 5:**After obtaining the correlation coefficient through Formula (4), the average of gray correlation is usually used as the degree of gray correlation.

#### 2.2. Proposed Hybrid Model

#### 2.2.1. CNN Model

#### 2.2.2. Bi-LSTM Model

- (1)
- LSTM Model

- (2)
- Bi-LSTM Model

#### 2.2.3. Hybrid Model

## 3. Case Study

#### 3.1. Data Process and Selection

#### 3.2. Results

#### 3.2.1. Data Set Division and Evaluation Indicators

#### 3.2.2. Experiments and Comparison

^{2}value of 0.9929. From Figure 9 and Figure 10, it can be also seen that the curve of BiLSTM-CNN model is the closest to the real wind power output curve and the prediction error is closer to zero than other models, especially in the time period when wind power changes drastically and rapidly. However, although the prediction accuracy is the highest, the computational speed of BiLSTM-CNN is the slowest with an average computational time of 0.4752 s. In addition, CNN has the fastest calculation speed among all the models with an average computational time of 0.0741 s. And compared with the hybrid model (CNN-BiLSTM, CNN-LSTM, BiLSTM-CNN, LSTM-CNN), all the single models (BiLSTM, LSTM, CNN) have a faster calculation speed with an average computational time 0.2260 s, 0.1274 s, 0.0741 s, respectively.

^{2}, Average computational time. Among them, the smaller the indicators RMSE, MSE, MAE and Average computational time, the better the model prediction performance while R

^{2}is the opposite.

^{2}0.9918. Compared with Bi-LSTM and LSTM, CNN has higher prediction accuracy and faster average operation speed, with IR (RMSE), IR (MSE), IR(MAE), IR(R2) and IR(Average computational time) all positive value.

^{2}, average computational time of CNN-BiLSTM are 24.13%, 54.09%, 32.64%, 0.44%, and−23.19%, respectively. Compared with the single model Bi-LSTM, the improved ratio of RMSE, MSE, MAE, R

^{2}, average computational time of BiLSTM-CNN are 31.50%, 72.92%, 40.32%, 0.53% and −52.44%, respectively. Compared with the single model LSTM, the improved ratio of RMSE, MSE, MAE, R

^{2}, average computational time of CNN-LSTM are 14.13%, 30.25%, 27.02%, −1.04%, −30.66%, respectively. Compared with the single model LSTM, the improved ratio of RMSE, MSE, MAE, R

^{2}, average computational time of LSTM-CNN are 33.35%, 77.81%, 47.59%, 0.60%, −53.12%, respectively. We can conclude that the CNN-BiLSTM and BiLSTM-CNN model all have a higher accuracy and lower running speed than single Bi-LSTM model and the CNN-LSTM and BiLSTM-CNN model has a higher accuracy and lower running speed than single LSTM model. It can be summarized that the ability of CNN to extract spatial features from multiple wind speed series at different height model is of great significance in improving the accuracy of short-term wind output prediction but hybrid models with CNN often need more computational time to mine the complex relationship between the input sequences and wind power output.

^{2}, average computational time of BiLSTM are 4.65%, 9.51%, 10.95%, 0.12% and −43.61%, respectively. Compared with the model CNN-LSTM, the improved ratio of RMSE, MSE, MAE, R

^{2}, average computational time of CNN-BiLSTM are 13.82%, 29.55%, 15.87%, 0.24% and −37.53%, respectively. Compared with the model LSTM-CNN, the improved ratio of RMSE, MSE, MAE, R

^{2}, average computational time of BiLSTM-CNN are 3.20%, 6.49%, 5.49%, 0.05%, −42.80%, respectively. It can be concluded that compared with LSTM, the Bi-LSTM model or the hybrid model containing Bi-LSTM has higher prediction accuracy in short-term wind power output prediction, because the bidirectional learning characteristic of Bi-LSTM can better mine the temporal feature between the multiple input time series data and historical wind power output time series. However, compared with LSTM, Bi-LSTM model or hybrid model has more parameters in the forecasting process, which results in the relatively low computational speed.

^{2}, average computational time of BiLSTM-CNN are 5.93%, 12.22%, 5.79%, 0.09% and −38.08%, respectively. Compared with the model CNN-LSTM, the improved ratio of RMSE, MSE, MAE, R

^{2}, average computational time of LSTM-CNN are 16.84%, 36.52%, 16.20%, 0.28% and −32.38%, respectively. From the results of the comparison, it can be concluded that BiLSTM-CNN has a higher prediction accuracy and a lower computational speed than CNN-BiLSTM; LSTM-CNN has a higher prediction accuracy and a lower computational speed than CNN-LSTM. To attain a higher prediction accuracy, it is better to ‘extracting the temporal characteristics of the input historical sequences first and extracting the spatial characteristics second’ than ‘extracting the spatial features among the input historical sequences first and then extracting the temporal features’.

#### 3.2.3. Further Study

## 4. Conclusions and Discussion

^{2}value of 0.9929. The hybrid model mainly predicts the short-term wind power output by taking advantage of the temporal-spatial features extraction ability of the proposed model to construct the complex relationship between the input data and target wind power output, where the Bi-LSTM model is utilized to mine the temporal characteristics of the input time series data and the convolution and pooling operations of CNN model is utilized to extract the spatial characteristics of multiple input time series data.

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 3.**Network structure of Bi-LSTM [43].

WP | WS (10 m) | WD (10 m) | WS (30 m) | WD (30 m) | WS (50 m) | WD (50 m) | |

Count | 4597 | 4597 | 4597 | 4597 | 4597 | 4597 | 4597 |

Mean | 38.91 | 4.55 | 118.79 | 5.20 | 120.60 | 5.45 | 120.62 |

Std | 33.81 | 2.35 | 99.14 | 2.69 | 101.10 | 2.80 | 100.66 |

Min | 0 | 0.13 | 0 | 0.17 | 0 | 0.13 | 0 |

Max | 143.09 | 15.86 | 360 | 19.15 | 360 | 19.64 | 360 |

WS (70 m) | WD (70 m) | WS (hub height) | WD (hub height) | Pressure (P) | Humidity (H) | ||

Count | 4597 | 4597 | 4597 | 4597 | 4597 | 4597 | |

Mean | 5.62 | 123.32 | 5.62 | 123.32 | 952.98 | 52.251817 | |

Std | 2.86 | 99.90 | 2.86 | 99.90 | 5.18 | 24.32 | |

Min | 0.15 | 0 | 0.15 | 0 | 941.34 | 4.01 | |

Max | 20.75 | 360 | 20.75 | 360 | 963.04 | 99.027 |

Standard Method | Grey Correlation Degree and Ranking Sequences | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

WS (10 m) | WD (10 m) | WS (30 m) | WD (30 m) | WS (50 m) | WD (50 m) | WS (70 m) | WD (70 m) | WS (Hub Height) | WD (Hub Height) | P | H | |

PDN | 0.811 | 0.610 | 0.800 | 0.652 | 0.800 | 0.710 | 0.767 | 0.708 | 0.767 | 0.708 | 0.778 | 0.577 |

Ranking: WS (10 m) > WS (30 m) > WS (50 m) > P > WS (70 m) > WS (hub height) > WD (50 m) > WD (70 m) > WD (hub height) > WD (30 m) > WD (10 m) > H | ||||||||||||

AVN | 0.761 | 0.587 | 0.758 | 0.608 | 0.742 | 0.662 | 0.712 | 0.677 | 0.712 | 0.677 | 0.636 | 0.492 |

Ranking: WS (10 m) > WS (30 m) > WS (50 m) > WS (70 m) > WS (hub height) > WD (70 m) > WD (hub height) > WD (50 m) > P > WD (30 m) > WD (10 m) > H |

Proposed Model | Configuration | |||
---|---|---|---|---|

BiLSTM-CNN | Bi-LSTM | Units1 | Units = 64; | Epoch = 80, Batch size = 100; Optimizer = ‘Adam’; Learning rate = 0.001. |

Units2 | Units = 128; | |||

Drop out | Drop out = 0.2 | |||

CNN | Convolution | Filter = 64; Kernel size = 3; Stride = 1 | ||

Max-pooling | Kernel size = 2; Stride = 1 | |||

Convolution | Filter = 128; Kernel size = 3; Stride = 1 | |||

Max-pooling | Kernel size = 2; Stride = 1 | |||

Drop out | Drop out = 0.1 | |||

Fully connected | Neurons = 512 |

Single Model | Hybrid Model | ||||||
---|---|---|---|---|---|---|---|

Bi-LSTM | LSTM | CNN | CNN-BiLSTM | CNN-LSTM | BiLSTM-CNN | LSTM-CNN | |

RMSE: | 3.3522 | 3.5079 | 2.7343 | 2.7005 | 3.0737 | 2.5492 | 2.6307 |

MSE: | 11.2369 | 12.3053 | 7.4766 | 7.2926 | 9.4475 | 6.4984 | 6.9204 |

MAE: | 2.4338 | 2.7004 | 1.8983 | 1.8349 | 2.1261 | 1.7344 | 1.8296 |

R^{2}: | 0.9877 | 0.9865 | 0.9918 | 0.9920 | 0.9896 | 0.9929 | 0.9924 |

Average computational time(s): | 0.2260 | 0.1274 | 0.0741 | 0.2942 | 0.1838 | 0.4752 | 0.2718 |

Description | |
---|---|

1st set comparison | CNN vs. Bi-LSTM; CNN vs. LSTM; CNN-BiLSTM vs. BiLSTM; BiLSTM-CNN vs. Bi-LSTM; CNN-LSTM vs.LSTM; LSTM-CNN vs. LSTM |

2nd set comparison | Bi-LSTM vs. LSTM; CNN-BiLSTM vs. CNN-LSTM; BiLSTM-CNN vs. LSTM-CNN |

3rd set comparison | BiLSTM-CNN vs. CNN-BiLSTM; LSTM-CNN vs. CNN-LSTM |

CNN vs. Bi-LSTM | CNN vs. LSTM | CNN-BiLSTM vs. BiLSTM | BiLSTM-CNN vs. Bi-LSTM | CNN-LSTM vs. LSTM | LSTM-CNN vs. LSTM | |
---|---|---|---|---|---|---|

IR(RMSE) | 22.59% | 28.29% | 24.13% | 31.50% | 14.13% | 33.35% |

IR(MSE) | 50.29% | 64.58% | 54.09% | 72.92% | 30.25% | 77.81% |

IR(MAE) | 28.21% | 42.26% | 32.64% | 40.32% | 27.02% | 47.59% |

IR(R^{2}) | 0.42% | 0.54% | 0.44% | 0.53% | −1.04% | 0.60% |

IR(Average computational time) | 204.90% | 71.92% | −23.19% | −52.44% | −30.66% | −53.12% |

Bi-LSTM vs. LSTM | CNN-BiLSTM vs. CNN-LSTM | BiLSTM-CNN vs. LSTM-CNN | |
---|---|---|---|

IR(RMSE) | 4.65% | 13.82% | 3.20% |

IR(MSE) | 9.51% | 29.55% | 6.49% |

IR(MAE) | 10.95% | 15.87% | 5.49% |

IR(R^{2}) | 0.12% | 0.24% | 0.05% |

IR(Average computational time) | −43.61% | −37.53% | −42.80% |

BiLSTM-CNN vs. CNN-BiLSTM | LSTM-CNN vs. CNN-LSTM | |
---|---|---|

IR(RMSE) | 5.93% | 16.84% |

IR(MSE) | 12.22% | 36.52% |

IR(MAE) | 5.79% | 16.20% |

IR(R^{2}) | 0.09% | 0.28% |

IR(Average computational time) | −38.08% | −32.38% |

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## Share and Cite

**MDPI and ACS Style**

Zhen, H.; Niu, D.; Yu, M.; Wang, K.; Liang, Y.; Xu, X.
A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction. *Sustainability* **2020**, *12*, 9490.
https://doi.org/10.3390/su12229490

**AMA Style**

Zhen H, Niu D, Yu M, Wang K, Liang Y, Xu X.
A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction. *Sustainability*. 2020; 12(22):9490.
https://doi.org/10.3390/su12229490

**Chicago/Turabian Style**

Zhen, Hao, Dongxiao Niu, Min Yu, Keke Wang, Yi Liang, and Xiaomin Xu.
2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction" *Sustainability* 12, no. 22: 9490.
https://doi.org/10.3390/su12229490