Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems

Abstract

The growing of the photovoltaic (PV) panel’s installation in the world and the intermittent nature of the climate conditions highlights the importance of power forecasting for smart grid integration. This work aims to study and implement existing Deep Learning (DL) methods used for PV power and electrical load forecasting. We then developed a novel hybrid model made of Feed-Forward Neural Network (FFNN), Long Short Term Memory (LSTM) and Multi-Objective Particle Swarm Optimization (MOPSO). In this work, electrical load forecasting is long-term and will consider smart meter data, socio-economic and demographic data. PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala. The second one is made of climate data collected at the climate management center in Douala. The performances of the models are evaluated using different error metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and regression (R). The proposed hybrid model gives a RMSE, MAE and R of 1.15, 0.75 and 0.999 respectively. The results obtained show that the novel deep learning model is effective in the both electrical load prediction and PV power forecasting and outperforms other models such as FFNN, Recurrent Neural Network (RNN), Decision Tree (DT), Gated Recurrent Unit (GRU) and eXtreme Gradient Boosting (XGBoost).

Keywords:

• Electrical demand prediction
• photovoltaic power forecasting
• deep learning models
• smart grid
• long short term memory
• multi-objective particle swarm optimization

REVIEWING EDITOR:

• Qingsong Ai, Wuhan University of Technology, China

SUBJECTS:

• Technology
• Computer Science
• Algorithms & Complexity
• Artificial Intelligence
• Computation
• Computer Engineering
• Computer Science (General)

Acknowledgements

The authors acknowledge the electrical engineering department of ENSET of University of Douala and the research team.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of this study are included within the article.

Additional information

Notes on contributors

Camille Franklin Mbey

Dr. Camille Franklin Mbey is a lecturer at ENSET of university of Douala. He received his PhD degree in 2021 at university of Douala. His research interests include smart grid, electrical power network, renewable power generation, artificial intelligence and deep learning. He can be contacted at email: [email protected].

Vinny Junior Foba Kakeu

Mr. Vinny Junior Foba Kakeu is an assistant lecturer at ENSET of university of Douala. He received his Master 2 degree in 2023 at university of Douala. His research interests include photovoltaic power generation, artificial intelligence, smart grid and deep learning. He can be contacted at email: [email protected].

Alexandre Teplaira Boum

Prof. Alexandre Teplaira Boum is a full professor at university of Douala since 2024. He receive his PhD degree in 2014. He is currently the head of departmet of basic sciences at university of Douala. His research interests include system optimization, smart power network, deep learning and Matlab programing. He can be contacted at email: [email protected].

Felix Ghislain Yem Souhe

Dr. Felix Gislain Yem Souhe is a lecturer at IUT of university of Douala. He received his PhD degree in 2022 at university of Douala. His research interests include electrical power grid, electrical consumption forecasting, artificial intelligence and smart grid. He can be contacted at email: [email protected].