Wind Power Forecasting: Advancing the Development of the Wind Power Industry

Wind power forecasting refers to the estimation and prediction of the power generation output of wind farms over a future period. It plays a critical role in wind farm operations and power grid dispatch, effectively improving the accuracy of wind power output predictions, reducing the risks associated with grid scheduling, and promoting the integration and development of wind power.

Keywords: Wind power forecasting, wind farms, grid dispatch, renewable energy

1. Introduction

Wind energy is a clean and renewable resource that plays an increasingly significant role in the global energy mix. However, the intermittent and variable nature of wind power poses challenges to the safe and stable operation of power grids. As a vital support for wind farm operations and grid dispatch, wind power forecasting enhances the accuracy of wind power output predictions, mitigates grid scheduling risks, and facilitates the integration and development of wind power.

2. Overview of Wind Power Forecasting

Wind power forecasting involves predicting wind power output over a future period based on factors such as wind resources and meteorological conditions. The primary methods of wind power forecasting include physical model forecasting, statistical model forecasting, and artificial intelligence model forecasting.

2.1 Physical Model Forecasting

Physical model forecasting is based on the fundamental principles of wind power generation, utilizing information such as wind resources and turbine parameters for calculations. Common physical model forecasting methods include:

l Power Curve Method: Calculates wind power output based on turbine power curves and wind speed forecasts.

l Wind Field Simulation Method: Uses computational fluid dynamics (CFD) software to simulate wind field speed distribution and combines it with turbine power curves to calculate wind power output.

2.2 Statistical Model Forecasting

Statistical model forecasting establishes statistical relationships between wind power and influencing factors using historical data for predictions. Common statistical model forecasting methods include:

l Time Series Forecasting: Utilizes time series models such as ARIMA and SARIMA to predict temporal changes in wind power.

l Regression Analysis: Builds regression models between wind power and influencing factors like wind speed, wind direction, temperature, and humidity for predictions.

2.3 Artificial Intelligence Model Forecasting

Artificial intelligence model forecasting is an emerging method developed in recent years, leveraging machine learning and deep learning technologies to uncover hidden patterns in wind power data for predictions. Common artificial intelligence model forecasting methods include:

l Artificial Neural Networks (ANN): Utilizes the powerful nonlinear fitting capabilities of neural networks to predict wind power.

l Support Vector Machines (SVM): Leverages the robust generalization capabilities of SVMs for wind power prediction.

l Random Forests: Employs the robustness of random forest models to predict wind power.

3. Influencing Factors of Wind Power Forecasting

The accuracy of wind power forecasting is affected by multiple factors, including:

l Wind Resource Conditions: Wind speed, direction, and vertical distribution are the primary factors influencing wind power forecasting.

l Turbine Parameters: Turbine type, capacity, power curves, and other parameters also impact forecasting accuracy.

l Meteorological Conditions: Temperature, humidity, atmospheric pressure, and other meteorological factors affect forecasting accuracy.

l Other Factors: Topography, vegetation cover, and other environmental factors also influence wind power forecasting.

4. Applications of Wind Power Forecasting

Wind power forecasting is widely applied in wind farm operations and grid dispatch:

4.1 Wind Farm Operations

Wind farms can optimize turbine output control strategies through wind power forecasting, improving the efficiency of power generation.

4.2 Grid Dispatch

Power grids can plan operational schedules in advance using wind power forecasting, enhancing grid stability and security.

4.3 Electricity Markets

Electricity markets can optimize trading strategies through wind power forecasting, improving market efficiency.

5. Development Trends in Wind Power Forecasting

As wind power technology advances, wind power forecasting methods are also evolving. Future developments in wind power forecasting will focus on the following directions:

l Improving Forecast Accuracy: Enhancing the spatial and temporal resolution of forecasts while reducing prediction errors.

l Reducing Forecasting Costs: Lowering hardware and software costs to improve cost-effectiveness.

l Increasing Forecast Reliability: Enhancing the robustness of forecasting methods against interference to ensure more reliable results.

6. Summary

Wind power forecasting is a critical supporting technology for the development of wind power. As wind power technology continues to advance, wind power forecasting methods will also improve, providing strong support for the integration and growth of wind power.