Marcos Vinicio Haas Rambo

Although Brazil is in the last mile of achieving universal electricity access, the Amazon region represents the country’s most critical electrification frontier, where remote energy expansion has largely failed to integrate clean alternatives. This study presents the design and techno-economic assessment of a hybrid off-grid microgrid tailored for the Amazon, incorporating a multi-source architecture that combines micro-hydro (MH), wind power (WT), solar photovoltaics (PV), diesel generation (DG), and battery energy storage systems (BESS). Three locations in Amazonas, Roraima, and Tocantins were evaluated under three scenarios: a hybrid system with DG, a fully renewable system without DG, and a green scenario that accounts for demand-side flexibility and selected technical and economic conditions. Optimization was performed using the HOMER tool for a typical isolated community. The hybrid configuration achieved a DG generation share of 0.7 (RSS, DG–WT–PV–MH–BESS), 0.9% (Jalapão, DG–WT–PV–MH–BESS), and 6.8% (Labrea, DG–PV–BESS). The fully renewable scenario resulted in Levelized Cost of Energy (COE) values ranging from 0.146 USD/kWh (RSS) to 0.388 USD/kWh (Lábrea), while the green scenario yielded COEs between 0.089 USD/kWh (RSS) and 0.190 USD/kWh (Lábrea). This study reveals the role of MH in reducing storage requirements, installed capacity, and system cost. The results are promising for presenting a competitive cost of energy.

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.