||Short-Term Wind Speed Forecasting Using Neural Network Models for Chanbin Offshore Area
||Department of Mechanical Engineering
Long Short-Term Memory
最後，本次研究在最後發現，每年春、秋、及冬季的風況大同小異。而夏季則容易受到不定期的西南季風以及颱風的侵襲影響。秋冬的風速平均較高 (~12m/s)，風向也很穩定（~18°）；春夏的風速平均較低 (~7m/s)且風向的位置較不特定。小波轉換得出的結果發現風況大致可以用東北風，西南風以及區域風去理解。利用類神經網路所建構出來的多步預測模型的準確率(相關係數 R^2)有達到 0.991，0.981，和 0.970。研究中的參數矯正雖在最後無顯著地提升模型的準確率，但卻可以提高我們對於預測模型的認知。
In 2016, Taiwan’s government published a new energy policy mandating making Taiwan a nuclear-free island by 2025. Offshore wind energy has become an important project to fill the energy gap after the shutdown of nuclear power plants. The biggest problem with wind power is wind instability. A precise forecasting model not only smooths the operation of wind farms, but also reduce damage to the power grid. In this study, wind data from 2017 to 2019 from the Taipower Meteorological Mast in Chanbin offshore is used in the analysis and model training. A short-term multi-step wind forecasting model is established based on an artificial neural network model and is optimized by changing the model parameters.
The data analysis is divided into three parts: a trend analysis, a statistical analysis, and a spectrum analysis. The trend analysis uses a time series plot and a wind rose plot to describe the trends in wind speed and direction over a three-year period. The statistical analysis explores the similarities and differences between each year using statistical values. Finally, the spectrogram obtained by the Wavelet Transform explains the features of the different wind sources. Based on the previous analyses, the accuracy and a further error analysis of the forecasting results are discussed. The LSTM model is the main model used in this study. The parameter tuning focuses on the input data in an attempt to obtain better performance from the data rather than from the model.
At the end of this study, we found the wind regime in spring, autumn, and winter to be similar every year and to be easily affected by the southwest monsoon and typhoons in summer. The average wind speed is high in autumn and winter but low in spring and summer. The Wavelet Transform results showed that the wind conditions can be roughly classified into northeast wind, southwest wind, and local wind. The accuracy (R^2 value) of the multi-step forecasting model reached 0.991, 0.981, and 0.970, respectively, in the first three step prediction. Although parameter tuning did not significantly improve the accuracy of the forecasting mode, it greatly improved our understanding of the forecasting result.
Table of Contents
Table of Contents i
List of Figures iii
List of Tables vii
1. Introduction 1
1.1. Background and Motivation 1
1.1.1. Taiwan 2
1.1.2. Wind forecasting 5
1.2. Research Objectives and Contributions 6
2. Literature Review 7
2.1. Meteorology 7
2.2. Wind regime in Taiwan 9
2.3. Power Curve 10
2.4. Wind Forecasting 11
3. Materials and Methods 13
3.1. Observation Site and Measurements 13
3.2. Statistical Analysis 15
3.3. Wavelet Spectrum Analysis 18
3.4. Forecasting Methods 20
3.4.1. Persistence Model 20
3.4.2. Artificial Neural Networks 20
3.4.3. Recurrent Neural Network 22
3.5. Neural Network Training Process 24
4. Results and Discussion 27
4.1. Data Sources 27
4.2. Data Analysis 27
4.2.1. Time Series Trends 28
4.2.2. Statistical Analysis 33
4.2.3. Wavelet Spectrum Analysis 37
4.2.4. Section Conclusions 48
4.3. Forecasting Results with the LSTM 49
4.3.1. Data Specifications and Hyperparameter Settings 50
4.3.2. Validation Results 52
4.3.3. Error Analysis 55
4.3.4. Section Conclusion 61
4.4. Forecasting Strategy 62
4.4.1. Model Tests 63
4.4.2. Lookback parameters 66
4.4.3. Look-forward Strategy 67
4.4.4. Wind Direction as a Variable 70
4.4.5. Time Average 73
4.4.6. Different Time Averages to Forecast 30-mins 77
4.4.7. Section Conclusion 80
5. Conclusions and Prospects 82
5.1. Conclusions 82
5.2. Prospects 83
6. References 84
7. Appendix 93
7.1. Details of Statistical Properties 93
7.2. Details of Small Scales Wavelet Spectrum in 2017 and 2018 96
7.3. Details of Diurnal Effects 102
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