风力发电功率预测模型数据

本数据集包含了详细的风力发电数据与预测结果数据，其中包括时间、测风塔不同高度的风速（10米、30米、50米和70米）、风向、温度、气压、湿度以及实际发电功率。这些数据可以用于训练机器学习模型，以预测未来的风力发电量。
通过深入分析与风力发电效率密切相关的气象数据，能够构建一个先进的预测模型。该模型采纳的关键输入参数包括风速、风向、温度、气压和湿度等环境特征，其核心目标则是精确预测即将到来的风力发电功率。借助这一模型，电力企业不仅能够实现电力资源的高效调度，还能够显著提升电网运行的智能化水平。最终，这将进一步增强风力发电的整体稳定性和经济效益，为推动可持续能源战略和实现绿色低碳经济提供强有力的技术支撑。

This dataset contains detailed wind power generation data and forecast result data, including time, wind speeds at different heights of the wind measurement tower (10m, 30m, 50m and 70m), wind direction, temperature, air pressure, humidity, and actual power generation output. These data can be used to train machine learning models for forecasting future wind power generation.
Through in-depth analysis of meteorological data closely related to wind power generation efficiency, an advanced forecasting model can be constructed. The key input parameters of this model include environmental features such as wind speed, wind direction, temperature, air pressure and humidity, and its core objective is to accurately predict the upcoming wind power generation output. With this model, power enterprises can not only achieve efficient dispatch of power resources, but also significantly enhance the intelligent level of power grid operation. Ultimately, this will further improve the overall stability and economic benefits of wind power generation, providing strong technical support for promoting the sustainable energy strategy and realizing the green and low-carbon economy.