This paper presents an evaluation of the use of deep learning architectures for forecasting electrical energy consumption in residential environments. The main contribution of this study lies in the development and assessment of a hybrid forecasting framework that integrates multiscale temporal analysis and weather data, enabling evaluation of predictive performance across different temporal granularities, forecast horizons, and aggregation levels. Single and hybrid models were compared, trained with high-resolution data from a single residence, both considering only endogenous variables and including exogenous variables (weather data). The results showed that, among all models tested in this study, the hybrid LSTM + GRU model achieved the highest predictive performance, with R² values of 94.62% using energy data and 95.25% when weather variables were included. Intermediary granularities, particularly the 6 steps, offered the best balance between temporal detail and predictive robustness for the tests performed. Furthermore, short-time windows aggregation (1 to 5 min) showed better accuracy, while the inclusion of weather data in scenarios with larger aggregation windows and longer horizons provided additional gains. The results reinforce the potential of hybrid deep learning models as effective tools for forecasting residential electricity consumption, with possible practical applications in energy management, automation, and integration of distributed energy resources.

Peer-to-peer (P2P) energy trading has emerged as a promising model for decentralised energy markets, allowing consumers to trade electricity directly. This paper explores the advances and challenges in P2P energy trading, with a historical review of its global development and a particular focus on Ireland. Despite technical advancements, several constraints hinder widespread adoption. Ireland lacks a clear legal framework to support P2P energy trading, creating regulatory uncertainty. Additionally, user acceptance remains low due to limited awareness, trust issues, and the complexities associated with participation. These challenges, combined with grid constraints and pricing uncertainties, have slowed progress. This paper shows that while P2P energy trading offers a decentralised, consumer-driven alternative to traditional energy markets, its success in Ireland will depend on regulatory evolution, technological diversification, and increased consumer engagement.

Peer-to-peer (P2P) energy trading has emerged as a promising model for decentralised energy markets, allowing consumers to trade electricity directly. This paper explores the advances and challenges in P2P energy trading, with a historical review of its global development and a particular focus on Ireland. Despite technical advancements, several constraints hinder widespread adoption. Ireland lacks a clear legal framework to support P2P energy trading, creating regulatory uncertainty. Additionally, user acceptance remains low due to limited awareness, trust issues, and the complexities associated with participation. These challenges, combined with grid constraints and pricing uncertainties, have slowed progress. This paper shows that while P2P energy trading offers a decentralised, consumer-driven alternative to traditional energy markets, its success in Ireland will depend on regulatory evolution, technological diversification, and increased consumer engagement.