Home > Research > Publications & Outputs > Short-Term Solar Irradiance Forecasting Model B...

Electronic data

  • Short_Term_Solar_Irradiance_Forecasting_Model_on_Bidirectional_Long_Short_Term_Memory_Deep (1)

    Rights statement: ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 1.02 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License


Text available via DOI:

View graph of relations

Short-Term Solar Irradiance Forecasting Model Based on Bidirectional Long Short-Term Memory Deep Learning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Publication date27/08/2021
Host publication2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
Number of pages6
ISBN (Electronic)9781665438971
ISBN (Print)9781665446020
<mark>Original language</mark>English


Recurrent neural networks (RNNs) are the most effective technology to study and analyze the future performance of solar irradiance. Bidirectional RNNs (BRNNs) provide the key benefit of manipulating the data with two different hidden layers in two opposite directions and can feed back to the same layer of output. In this approach, the output layers can simultaneously receive information from the past (backward layers) and the future (forward layers). A bidirectional long short-term memory (BI-LSTM) model was developed and employed to predict solar irradiance values for the next 169 hours based on hourly historical data (01-01-1985 to 16-09-2020) from Tabuk city. The findings specifically demonstrate that in terms of classification and considerations, the BI-LSTM model has promising performance with notable accuracy. In addition, the model is capable of coping with the selected size of sequential data. The prediction accuracy and performance of the BI-LSTM model were highly enhanced when external data such as wind speed and temperature were provided