Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

Jessica Wojtkiewicz, Matin Hosseini, Raju Gottumukkala and Terrence Lynn Chambers
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Jessica Wojtkiewicz: College of Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Matin Hosseini: School of Computing & Informatics, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Raju Gottumukkala: College of Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA
Terrence Lynn Chambers: College of Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA

Energies, 2019, vol. 12, issue 21, 1-13

Abstract: 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.

Keywords: solar irradiance; time series forecasting; gated recurrent units; deep learning; multivariate (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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