Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models Article Swipe

MAKE IMPACT

Irfan Jamil , Lucheng Hong , Sheeraz Iqbal , Muhammad Aurangzaib , Rehan Jamil , Hossam Kotb , Abdulaziz Alkuhayli , Kareem M. AboRas · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1016/j.aej.2023.06.023

The advanced development of large-scale solar power plants (LSSPs) has made it necessary to improve accurate forecasting models for the output of solar energy. Solar energy is still hampered by the lack of predictability in its output, which remains a major hurdle in the solar industry. This paper focuses on triple deep learning (DL) techniques such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolutional Neural Network- Long-Short Term Memory CNN-LSTM to address this problem. These techniques are utilized in solar energy variables (SEVs) such as power generation (MWh), soiling loss (%), and performance ratio (PR %) to determine the optimal forecast model. The novelty of this research is that it is the first time that important solar system parameters such as PR and soiling loss have been studied to predict a feasible forecast model using a different DL scheme. The SEVs real-time dataset is procured from the largest solar plant in Pakistan, titled “Quaid-e-Azam Solar Park” (QASP). The main significance of the study is that ANN, RNN, and CNN-LSTM-based models were developed in the DL process through feature generation, data scaling, training, and testing steps to predict the optimal model. The prediction values were compared with the solar plant's actual values over the last 7 years, and then a comparison was made to predict the future forecast trend over the next 20 years. The aim and goal is to develop three models to investigate the accurate results of time-series forecasting on the SEVs dataset, as well as to evaluate the performance measure errors to determine the appropriate model. Based on the forecasting/prediction graphic and error results, it was demonstrated that the CNN-LSTM hybrid model is a more capable forecasting model for the output of power generation and PR values and a more accurate predictor of the future trend over the ANN and RNN models. However, the ANN model is slightly better performed in predicting of soling loss value than CNN-SLTM and RNN. Thus, the CNN-LSTM hybrid model is an optimal model for SEVs, which can guarantee a variety of LSSPs of similar nature in the following time-series forecasting investigations which shows key findings and its importance for the solar industrial forecast issue.

Type

article

Language

en

Landing Page

https://doi.org/10.1016/j.aej.2023.06.023

OA Status

gold

Cited By

31

References

78

Related Works

10

OpenAlex ID

https://openalex.org/W4381050922

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4381050922

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.1016/j.aej.2023.06.023

Digital Object Identifier

Title

Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models

Work title

Type

article

OpenAlex work type

Language

en

Primary language

Publication year

2023

Year of publication

Publication date

2023-06-17

Full publication date if available

Authors

Irfan Jamil, Lucheng Hong, Sheeraz Iqbal, Muhammad Aurangzaib, Rehan Jamil, Hossam Kotb, Abdulaziz Alkuhayli, Kareem M. AboRas

List of authors in order

Landing page

https://doi.org/10.1016/j.aej.2023.06.023

Publisher landing page

Open access

Yes

Whether a free full text is available

OA status

gold

Open access status per OpenAlex

OA URL

https://doi.org/10.1016/j.aej.2023.06.023

Direct OA link when available

Concepts

Artificial neural network, Predictability, Solar energy, Recurrent neural network, Solar power, Deep learning, Computer science, Artificial intelligence, Convolutional neural network, Scale (ratio), Photovoltaic system, Machine learning, Meteorology, Engineering, Power (physics), Statistics, Mathematics, Quantum mechanics, Physics, Electrical engineering

Top concepts (fields/topics) attached by OpenAlex

Cited by

31

Total citation count in OpenAlex

Citations by year (recent)

2025: 13, 2024: 16, 2023: 2

Per-year citation counts (last 5 years)

References (count)

78

Number of works referenced by this work

Related works (count)

10

Other works algorithmically related by OpenAlex

Full payload