论文标题
通过机器学习算法的短期太阳能预测
Short term solar energy prediction by machine learning algorithms
论文作者
论文摘要
太阳能站的平稳发电需要准确，可靠和有效的太阳能预测，以最佳整合以满足市场需求；但是，太阳能生产的隐含不稳定可能会给平稳的发电带来严重的问题。我们通过利用机器学习技术的强度来捕获和分析巨大特征的复杂行为，每天报告太阳能的预测。为此，由美国气象学会（AMS）的能源竞争占据了包括98个太阳能站的数据集，用于预测日常太阳能。在AMS太阳能数据集上已经实施了基础线回归器的预测模型，包括线性，脊，套索，决策树，随机森林和人工神经网络。网格大小转换为两个部分：16x9和10x4，以确定属性对来自全球集合预测系统（GEFS）的密集位置的生成的电源有更多的影响。为了评估模型，已经分析了RMSE，MAE和R2_SCORE方面的预测误差的统计度量，并与现有技术进行了比较。已经观察到，与所有其他提出的方法相比，通过随机森林和脊回归来提高准确性。在单个太阳能站和多个独立运行中评估了所提出的方案的稳定性和可靠性。
Smooth power generation from solar stations demand accurate, reliable and efficient forecast of solar energy for optimal integration to cater market demand; however, the implicit instability of solar energy production may cause serious problems for the smooth power generation. We report daily prediction of solar energy by exploiting the strength of machine learning techniques to capture and analyze complicated behavior of enormous features effectively. For this purpose, dataset comprising of 98 solar stations has been taken from energy competition of American Meteorological Society (AMS) for predicting daily solar energy. Forecast models of base line regressors including linear, ridge, lasso, decision tree, random forest and artificial neural networks have been implemented on the AMS solar dataset. Grid size is converted into two sections: 16x9 and 10x4 to ascertain attributes contributing more towards the generated power from densely located stations on global ensemble forecast system (GEFS). To evaluate the models, statistical measures of prediction error in terms of RMSE, MAE and R2_score have been analyzed and compared with the existing techniques. It has been observed that improved accuracy is achieved through random forest and ridge regressor for both grid sizes in contrast to all other proposed methods. Stability and reliability of the proposed schemes are evaluated on a single solar station as well as on multiple independent runs.
