Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units
Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units
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DOI：
10.3390/en12214055
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发表时间：
2019-10
期刊：
影响因子：
3.2
通讯作者：
Jessica Wojtkiewicz;Matin Hosseini;Raju N. Gottumukkala;T. Chambers
中科院分区：
文献类型：
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作者：
Jessica Wojtkiewicz;Matin Hosseini;Raju N. Gottumukkala;T. Chambers
太阳辐照度的变化导致太阳能发电厂的发电波动。电网运营商需要准确的辐照度预测来管理这种变化。许多因素影响辐照度，包括一年中的时间，天气和一天中的时间。云量是影响太阳能发电的最重要的变量之一，但也具有高度的可变性和不确定性。深度学习方法能够学习序列数据中的长期依赖关系。我们研究应用门控回归单元（GRU）预测太阳辐照度和应用多元GRU预测每小时太阳辐照度在亚利桑那州凤凰城的结果。我们使用严格的历史太阳辐照度数据以及添加外源天气变量和云量数据，比较和评估GRU与长短期记忆（LSTM）的性能。根据我们的研究结果，我们发现在GRU和LSTM中添加外生天气变量和云量数据显着提高了预测精度，表现优于单变量和统计模型。
Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but is also characterized by a high degree of variability and uncertainty. Deep learning methods have the ability to learn long-term dependencies within sequential data. We investigate the application of Gated Recurrent Units (GRU) to forecast solar irradiance and present the results of applying multivariate GRU to forecast hourly solar irradiance in Phoenix, Arizona. We compare and evaluate the performance of GRU against Long Short-Term Memory (LSTM) using strictly historical solar irradiance data as well as the addition of exogenous weather variables and cloud cover data. Based on our results, we found that the addition of exogenous weather variables and cloud cover data in both GRU and LSTM significantly improved forecasting accuracy, performing better than univariate and statistical models.