论文标题
根据预测和纠正维护活动预测太阳能发电
Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities
论文作者
论文摘要
在过去的十年中，使用从气象站收集的历史时间序列，例如天气变量风速和方向，太阳辐射和温度。它有助于太阳能发电厂的整体管理。但是，太阳能发电厂定期需要预防和纠正性维护活动，以进一步影响能源生产。本文介绍了一项新的工作，用于预测基于维护活动，发电厂观察到的问题以及天气数据的太阳能能源生产。从1MW太阳能发电厂（我们的大学）获得的数据集上完成的结果，该数据集生成了具有13列的数据集，该列是从2012年到2020年的每日条目。有12个结构化的列和一个非结构化的列，其中包含有关不同维护活动，观察到的不同维护活动，观察到的问题的手动文本条目。非结构化列用于使用哈希映射，标志单词和停止单词创建新功能列向量。最终数据集包括基于相关性和因果分析的五个重要特征向量列。
Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, and temperature. It helps in the overall management of solar power plants. However, the solar power plant regularly requires preventive and corrective maintenance activities that further impact energy production. This paper presents a novel work for forecasting solar power energy production based on maintenance activities, problems observed at a power plant, and weather data. The results accomplished on the datasets obtained from the 1MW solar power plant of PDEU (our university) that has generated data set with 13 columns as daily entries from 2012 to 2020. There are 12 structured columns and one unstructured column with manual text entries about different maintenance activities, problems observed, and weather conditions daily. The unstructured column is used to create a new feature column vector using Hash Map, flag words, and stop words. The final dataset comprises five important feature vector columns based on correlation and causality analysis.
