The use of renewable energies, such as Photovoltaic (PV) solar power, is necessary to meet the growing energy consumption. PV solar power generation has intrinsic characteristics related to the climatic variables that cause intermittence during the generation process, promoting instabilities and insecurity in the electrical system. One of the solutions for this problem uses methods for the Prediction of Solar Photovoltaic Power Generation (PSPPG). In this context, the aim of this study is to develop and compare the prediction accuracy of solar irradiance between Artificial Neural Network (ANN) and Long-Term Short Memory (LSTM) network models, from a comprehensive analysis that simultaneously considers two distinct sets of exogenous meteorological input variables and three short-term prediction horizons (1, 15 and 60 min), in a controlled experimental environment. The results indicate that there is a significant difference (p < 0.001) in the prediction accuracy between the ANN and LSTM models, with better overall prediction accuracy skill for the LSTM models (MAPE = 19.5%), except for the 60 min prediction horizon. Furthermore, the accuracy difference between the ANN and LSTM models decreased as the prediction horizon increased, and no significant influence was observed on the accuracy of the prediction with both sets of evaluated meteorological input variables.

With the current worsening of climate change-associated risks, the transition to low-carbon energy sources has become a global priority. In this context, the advances in the implementation of smart grids, which, in addition to greater efficiency and resilience, also allow greater penetration of renewable distributed energy resources, are becoming increasingly important. However, the necessary investments will be colossal. Many specialists see the process of opening up the electric energy markets as essential to boosting these new technologies. Greater decentralization of the decision-making process can potentially promote greater scalability. However, not all liberalization reforms have led to good results. Several researchers have been evaluating experiences in different countries. Brazil, a country with continental dimensions and peculiar characteristics, already counts with a mostly renewable electric energy generation mix. In recent decades, however, it has become increasingly dependent on fossil fuel sources. Brazil has been conducting a process of opening the electric energy market since the 1990s. This process has faced a series of barriers. This article presents a critical bibliographic review of the Brazilian Power System history and its ongoing opening process, its possible successes and errors, as well as its perspectives and challenges.

Solar photovoltaic energy has experienced significant growth in the last decade, as well as the challenges related to the intermittency of power generation inherent to this process. In this paper we propose to perform short-term forecasting of solar PV generation using fuzzy time series (FTS). Two FTS methods are proposed and evaluated to obtain a global horizontal irradiance (GHI) value. The first is the weighted method and the second is the fuzzy information granular method. Using the direct proportionality of the power with the GHI, the spatial smoothing process was applied, obtaining spatial irradiance on which a first-order low pass filter was applied to simulated power photovoltaic system generation. Thus, this study proposed indirect and direct forecasting of solar photovoltaic generation which was statistically evaluated and the results showed that the indirect prediction showed better performance with GHI than the power simulation. Error statistics, such as RMSE and MBE, show that the fuzzy information granular method performs better than the weighted method in GHI forecasting.

Sustainable mobility in university environments presents both a challenge and an opportunity to reduce environmental impact and promote energy efficiency. This study assesses the feasibility of implementing electric buses in the internal transportation system of the Federal University of Paraíba (UFPB), considering environmental, economic, and operational aspects. The analysis demonstrates that transitioning to this model can lead to a significant reduction in greenhouse gas (GHG) emissions, noise pollution mitigation, and optimization of operational costs throughout the vehicle’s life cycle. The study examines technical, structural, and financial factors, emphasizing the necessary infrastructure, academic community acceptance, and the economic viability of the project, as well as the strategic advantage of integrating the electric fleet with photovoltaic energy generation. The key highlights of this research include: (i) Sustainability and energy efficiency, emphasizing a reduction of up to 52.52% in CO₂ emissions when vehicles are powered by photovoltaic energy in an LCA context, alongside improvements in air quality and noise pollution mitigation. (ii) Economic feasibility analysis, comparing operational and maintenance costs between electric and conventional diesel buses, evaluating the financial viability and potential return on investment. (iii) Infrastructure and implementation challenges, addressing the need for charging stations, adaptation of UFPB’s infrastructure, and financing models, including government subsidies and strategic partnerships. (iv) Impact on the academic community, analyzing student and staff perceptions and acceptance of fleet electrification and the promotion of sustainable practices. (v) Future projections and replicability, exploring trends in the sustainable transportation sector, as well as the potential expansion of the electric fleet and its integration with energy storage systems. The results indicate that adopting electric buses at UFPB can position the institution as a benchmark in sustainable mobility, serving as a replicable model for other universities and contributing to carbon emission reduction and modernization of university transportation.

Inequality and the lack of basic services are problems that affect some regions of the Amazon. Among these services, electricity is considered essential for quality of life, but it is still scarce. In some cases, the absence of electricity brings with it concerns that impact human health, well-being, and development. In this context, this research proposes to develop the sizing of a modular and expandable system for generating electricity with off-grid energy storage to serve single-family homes of river dwellers (from 2 to 8 people) in isolated communities in the Amazon. The research presents and demonstrates the Proknow-C systematic methodology, which shows a systematic approach to rigorous and structured literature reviews. The Amazon Kit concept covers the systems and configurations that can be proposed for single-family homes in the Amazon. The sizing of the Amazon Kit is carried out, ranging from data mapping to estimating consumption per person in homes, followed by the analytical calculation of the solar photovoltaic system—off the grid, considering the basis of the CRESESB portal. SAM (version 2023.12.17) and HOMER PRO^® (Version 3.16.2) software is used to simulate and validate the systems. Thus validating the sizing and configuration according to the mapped data and per capita consumption and validating the operability and functionality according to the operating regime, respectively. In this manner, the system depicted in the design and specifications can be adapted to the requirements of single-family dwellings. Furthermore, it offers convenient system maintenance, with an inverter that operates in various configurations (on, off, and zero grid), as well as energy storage for days without sunlight or system maintenance. As a result, the system uses renewable technologies to provide electricity services, filling a significant gap in the literature found in the research. It also offers a sustainable and affordable solution to improve the quality of life and reduce dependence on non-renewable sources.