基于本地历史天气数据和全球预报的局部太阳能预测
Localized solar power prediction based on weather data from local history and global forecasts
AI总结：
本文提出利用时间序列模型结合外生输入，结合本地历史天气和全球预报数据，实现18小时提前预测，准确率达约80%。
摘要：
随着商业建筑净零可持续性需求的增加，光伏资产的整合变得更加重要。然而，由于高太阳变化性和光伏输出预测的不确定性，这种整合仍具挑战性。现有方法通常仅使用本地电力/天气历史或全球天气预报，忽略全球现象或本地特征。本文提出利用本地历史天气和全球预报数据，基于时间序列建模与外生输入，实现18小时提前预测，准确率达约80%，使用美国国家海洋和大气管理局（NOAA）的高分辨率快速刷新（HRRR）模型数据。
英文摘要：
With the recent interest in net-zero sustainability for commercial buildings, integration of photovoltaic (PV) assets becomes even more important. This integration remains a challenge due to high solar variability and uncertainty in the prediction of PV output. Most existing methods predict PV output using either local power/weather history or global weather forecasts, thereby ignoring either the impending global phenomena or the relevant local characteristics, respectively. This work proposes to leverage weather data from both local weather history and global forecasts based on time series modeling with exogenous inputs. The proposed model results in eighteen hour ahead forecasts with a mean accuracy of $\approx$ 80\% and uses data from the National Ocean and Atmospheric Administration's (NOAA) High-Resolution Rapid Refresh (HRRR) model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
