Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying what form the variation should take and allow the extraction of high-level features. The DRNN is used to predict the irradiance. The data utilized in this study is real data obtained from natural resources in Canada. The simulation of this method will be compared to several common methods such as support vector regression and feedforward neural networks (FNN). The results show that deep learning neural networks can outperform all other methods, as the performance tests indicate.

Meeting Name

Complex Adaptive Systems Conference: Engineering Cyber Physical Systems (2017: Oct. 30-Nov. 1, Chicago, IL)


Electrical and Computer Engineering

Second Department

Engineering Management and Systems Engineering

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Adaptive Systems; Big Data; Complex Networks; Electric Utilities; Embedded Systems; Feedforward Neural Networks; Forecasting; Neural Networks; Photovoltaic Cells; Recurrent Neural Networks; Renewable Energy Resources; Solar Radiation; Support Vector Machines; Autoregressive Moving Average; DRNN; Irradiance; Learning Neural Networks; Power; Renewable Energy Generation; Solar; Support Vector Regression (SVR); Deep Neural Networks; DNN; PV

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2017 Elsevier, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publication Date

01 Nov 2017