AI-Powered Framework for Predicting Renewable Energy in Peer-to-Peer Trading

This study embarks on a critical examination of future energy demand forecasting by utilizing a unique dataset that encapsulates energy usage patterns within the Education City Community Housing (ECCH). As urban areas grapple with the challenges of energy distribution and consumption, the integration of advanced predictive analytics becomes paramount. By adopting cutting-edge deep learning techniques, this research aims not only to enhance the efficiency of peer-to-peer (P2P) energy systems but also to mitigate issues associated with congestion and energy losses often seen in conventional grids. The methodology encompasses three sophisticated machine learning algorithms - Bidirectional Long-Short-Term Memory (Bi-LSTM), Random Forest (RF), and Gated Recurrent Unit (GRU) - each meticulously chosen to forecast energy consumption metrics specific to the community's needs. Evaluating model performance through metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), the research provides an insightful comparison of these algorithms in predicting energy demand. Findings underscore a significant predictive capability, particularly highlighting the GRU and Bi-LSTM models, which demonstrate exceptional accuracy when applied as univariate models focused solely on energy consumption data. This emphasis on energy-centric modeling not only contributes to a deeper understanding of consumption patterns within ECCH but also enhances the potential for implementing tailored energy solutions and fostering sustainable practices within the community. The implications of this study extend beyond mere prediction; they pave the way for smarter energy management strategies that could ultimately lead to a more sustainable and efficient future for urban living environments.