The transition from conventional methods of energy production to renewable energy production necessitates better prediction models of the upcoming supply of renewable energy. In wind power production, error in forecasting production is impossible to negate owing to the intermittence of wind. For successful power grid integration, it is crucial to understand the uncertainties that arise in predicting wind power production and use this information to build an accurate and reliable forecast. This can be achieved by observing the fluctuations in wind power production with changes in different parameters such as wind speed, temperature, and wind direction, and deriving functional dependencies for the same. Using optimized machine learning algorithms, it is possible to find obscured patterns in the observations and obtain meaningful data, which can then be used to accurately predict wind power requirements . Utilizing the required data provided by the Gamesa's wind farm at Bableshwar, the paper explores the use of both parametric and the non-parametric models for calculating wind power prediction using power curves. The obtained results are subject to comparison to better understand the accuracy of the utilized models and to determine the most suitable model for predicting wind power production based on the given data set.
翻译:从传统的能源生产方法过渡到可再生能源生产,需要更好地预测未来可再生能源供应的预测模型。在风力发电中,预测生产中的错误不可能因风的干扰而消除。为了成功地整合电网,至关重要的是要了解在预测风力发电过程中出现的不确定性,并利用这一信息进行准确可靠的预测。通过观测风力生产中的波动,同时改变风速、温度和风向等不同参数,并由此得出功能依赖性。利用优化的机器学习算法,有可能在观测中找到模糊的模式,并获得有意义的数据,这些数据随后可用于准确预测风力需求。利用奥培拉在贝布什瓦尔的风场提供的所需数据,本文探索使用参数模型和非参数模型来利用电曲线计算风力预测。取得的结果可以进行比较,以便更好地了解所用模型的准确性,并确定以特定数据集为基础预测风力生产的最合适的模型。