Time Series Forecasting on Solar Irradiation using Deep Learning

Sorkun M. C. , Paoli C., DURMAZ İNCEL Ö.

10th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Türkiye, 30 Kasım - 02 Aralık 2017, ss.151-155 identifier

  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.151-155


Time series forecasting is currently used in various areas. Energy management is also one of the most prevalent application areas. As a matter of fact, energy suppliers and managers have to face with the energy mix problem. Electricity can be produced from fossil fuels, from nuclear energy, from bio-fuels or from renewable energy resources. Concerning electricity generation system based on solar irradiation, it is very important to know precisely the amount of electricity available for the different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar irradiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium to longterm horizons. On this paper we focus only on statistical time series forecasting methods. The aim of this study is to assess if deep learning can be suitable and competitive on the solar irradiation data time series forecasting. In this context, studies using deep learning and other machine learning methods for time series forecasting were investigated. A special Recurrent Neural Network variations Long Short-Term Memories and Gated Recurrent Unit models are introduced.