Special Issue on AI-empowered Smart Grid Technologies and EVs 2024

Forecasting solar PV power output holds significant importance in the realm of energy management, particularly due to the intermittent nature of solar irradiation. Currently, most forecasting studies employ statistical methods. However, deep learning models have the potential for better forecasting. This study utilises Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and hybrid LSTM-GRU deep learning techniques to analyse, train, validate, and test data from the Zagtouli Solar Photovoltaic (PV) plant located in Ouagadougou (longitude:12.30702o and latitude:1.63548o), Burkina Faso. The study involved three evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The RMSE evaluation criteria gave 10.799(LSTM), 11.695(GRU) and 10.629(LSTM-GRU) giving the LSTM-GRU model as the best for RMSE evaluation. The MAE evaluation provided 2.09, 2.1 and 2.0 for the LSTM, GRU and LSTM-GRU models respectively, showing that the LSTM-GRU model is superior for MAE evaluation. The R2 criteria similarly showed the LSTM-GRU model to be best with 0.999 compared to 0.998 for LSTM and 0.997 for GRU. It becomes evident that the hybrid LSTM-GRU model exhibits superior predictive capabilities compared to the other two models. These results indicate that the hybrid LSTM-GRU model has the potential to reliably predict the solar PV power output. It is therefore recommended that the authorities in charge of the solar PV Plant in Ouagadougou should consider switching to the deep learning LSTM-GRU model.

The rapid expansion of digital universities across Africa addresses the need for scalable higher education solutions, but challenges such as limited physical infrastructure and high dropout rates persist. In digital learning environments, effective interaction with educational content is crucial for student success. This article explores the transformative role of chatbots integrated into digital university platforms, with a specific focus on their impact on learner-content interactions. Leveraging the frequent use of messaging applications and advances in Artificial Intelligence (AI), we examine how chatbot integration enhances student engagement, facilitates personalized access to core educational modules, and supports formative assessments to reinforce learning outcomes. Using the Rasa open-source framework and the Moodle Learning Management System (LMS), we present a model that not only delivers content efficiently but also provides an interactive learning experience through AI-driven dialogue systems. Furthermore, a comparison of the different AI tools used for educational chatbots will be presented, to determine the most suitable solutions for digital teaching. This analysis will consider various aspects such as efficiency, customization, flexibility and ease of integration of the tools into educational environments. This study highlights how chatbots can foster a more dynamic and responsive learning ecosystem, ultimately improving student retention and mastery of key concepts in digital universities. In this article, we explore the broader impact of chatbots on learner interaction with educational content, not just their integration. It also emphasizes student engagement and retention.