Accurate energy consumption forecasting is becoming increasingly important due to rising global energy demands driven by economic development and population growth. Traditional forecasting models often overlook the impact of contextual factors, such as weather conditions and occupancy trends, which are essential for precise predictions. In this study, we propose a hybrid context-aware simulated scenario generation (CA-SSG) approach that integrates context space theory (CST) with deep learning techniques. This method leverages key contextual features to generate synthetic energy consumption data that more accurately mimics real-world patterns. Using the ASHRAE Great Energy Predictor III dataset, which includes diverse building types across various climates, we demonstrate the effectiveness of CA-SSG. The results show significant improvements in model performance, with reductions in Kullback-Leibler divergence (5%), increases in Pearson Correlation Coefficient (5%), and decreases in computation time compared to traditional approaches. These findings highlight the advantages of contextually enriched generative models for developing smarter energy management systems, enabling more accurate energy forecasting, and supporting strategic planning for energy consumption.

Online learning has yielded numerous advantages, notably enhanced accessibility and resource efficiency, which have played a vital role in sustaining educational continuity amidst unprecedented challenges, such as the COVID-19 pandemic. Despite the various benefits and opportunities provided by online learning, many challenges need to be addressed. For instance, the virtual learning environment may introduce potential barriers to effective communication and interaction. It has been established that genuine student engagement is pivotal for effective learning, surpassing the mere availability of high-quality educational materials. Utilizing deep learning (DL) architectures, we harness artificial intelligence (AI) to propose a multimodal approach for assessing and evaluating student engagement in online learning environments. Our results are promising, showcasing the potential impact of AI in enhancing online learning experiences for both students and educators. Additionally, we present an emotion classifier that outperforms the widely recognized DeepFace emotion recognition model on the test set, increasing accuracy from 54% to 72%. We aspire to stimulate further research in this direction, as the ongoing shift towards digital and online learning necessitates innovative solutions to ensure that educational outcomes remain robust and equitable for all learners.