Wind Power Generation vs Oceanic indices in Sri Lanka

This dataset provides a comprehensive record of wind power generation and its relationship with oceanic-atmospheric indices, facilitating advanced forecasting and analytical research in renewable energy. The dataset comprises 12 input parameters, including average wind speed, which serves as a crucial predictor, while wind power generation acts as the output variable. The data spans from January 2015 to December 2019, covering a total of 33 monthly samples. The wind power generation exhibits distinct seasonal patterns and long-term trends, with the highest power output observed in June 2018 and the lowest in December 2019.To enhance predictive modeling, the dataset integrates monthly oceanic indices, derived through an analysis of their day-to-day fluctuations, capturing their potential lead-lag influence on wind power generation. The use of monthly-scale indices aligns with the power plant's operational frequency, enabling correlation-based insights into atmospheric drivers. While the dataset does not explicitly model the complex physics of atmospheric interactions, it attributes wind speed variations—the primary determinant of power output—to broader atmospheric phenomena. The high data quality and discernible trends make this dataset an essential resource for researchers exploring wind power forecasting, climate-energy interactions, and machine learning applications in renewable energy. 

本数据集全面记录了风力发电情况及其与海洋-大气指数（oceanic-atmospheric indices）的关联关系，可为可再生能源领域的先进预测与分析研究提供有力支撑。数据集包含12项输入参数，其中平均风速为核心预测因子，风力发电则作为模型的输出变量。数据时间跨度为2015年1月至2019年12月，共计33组月度样本。该风力发电数据呈现出鲜明的季节特征与长期变化趋势，2018年6月发电量达到峰值，2019年12月则为全年谷值。为优化预测建模效果，本数据集纳入了月度海洋指数：这类指数通过对其每日波动情况进行分析生成，可捕捉其对风力发电的潜在超前-滞后影响。月度尺度的海洋指数与该电厂的运行频率相契合，能够帮助研究者基于相关性分析深入挖掘大气驱动因素。尽管本数据集未对大气相互作用的复杂物理机制进行显式建模，但它将作为发电量核心决定因素的风速变化，归因于更广泛的大气现象。本数据集兼具高质量数据与清晰可辨的变化特征，是开展风力发电预测、气候-能源交互研究以及可再生能源领域机器学习应用的科研人员不可或缺的核心资源。