José Manuel Alvarez Alvarado

Deep learning solar forecasters depend strongly on their hyperparameters, yet comparisons of hyperparameter optimization methods usually rely on few runs per optimizer, confounding the optimizer with the random seed. A protocol is proposed that tests whether optimizers are practically equivalent and how reliable published comparison designs are. Seven methods (four metaheuristics, two model-based methods, one of them multi-fidelity, and Random Search) tuned three deep networks for one-hour-ahead forecasting of global horizontal irradiance in Querétaro, Mexico, with public reanalysis data, a shared search space, one hundred evaluations, and thirty paired seeds, in nine hundred and thirty runs across all experiments. The tuned models improved on smart persistence by 4–11% in mean absolute error. With the 10-epoch search objective, no pair of optimizers differed after Holm correction, 62 of 63 pairs were equivalent within 1 W/m², and the optimizer explained 0.8% of the error variance, compared with 7.9% for the seed. In a higher-fidelity check with 50 epochs, two guided methods improved on Random Search by 0.74 W/m², significantly for one of them, while remaining equivalent to each other, suggesting that, in this setting, objective fidelity mattered more than the choice among guided optimizers. In emulations of published designs, even with 30 runs, 2 studies agreed on the winner in at most 55% of cases. A validated surrogate benchmark, code, data, and predictions are released.

Artificial intelligence (AI) is now a fundamental tool in the development of engineering solutions for prediction, diagnosis, optimization, monitoring, control, and decision support

Learning-based approaches have significantly advanced the capabilities of Simultaneous Localization and Mapping (SLAM) systems, particularly in challenging environments characterized by noise, dynamic objects, and perceptual ambiguity. However, the literature remains highly heterogeneous in terms of sensing modalities, datasets, evaluation protocols, and reporting practices, making systematic comparison difficult. This paper presents a taxonomy-driven review of learning-based SLAM approaches, with particular emphasis on LiDAR-based systems in mobile robotics, and introduces a functional taxonomy that categorizes methods according to the role of learning within the SLAM architecture: (i) learning-enhanced front-end SLAM (T1), (ii) learning-enhanced back-end SLAM (T2), and (iii) learning-centric SLAM systems (T3). Representative studies were analyzed with respect to performance characteristics, robustness, computational requirements, datasets, and deployment-related evidence. The analysis shows that T1 approaches primarily improve local pose estimation and robustness, T2 methods enhance global consistency through learning-based loop closure and relocalization, and T3 approaches explore unified representations, semantic reasoning, and learning-centric autonomy, albeit with greater computational demands and limited deployment evidence. The review further indicates that hybrid approaches combining geometric and learning-based components constitute a prominent trend in the literature, frequently reporting improvements in accuracy and adaptability while maintaining compatibility with established SLAM frameworks. Nevertheless, these observations should be interpreted cautiously, as stronger empirical evidence for hybrid systems may partially reflect their greater technological maturity and broader evaluation history. Finally, the review identifies persistent challenges, including limited cross-domain generalization, high computational requirements, limited deployment-oriented evaluation, and the lack of standardized benchmarking and reporting practices. These findings highlight the need for more reproducible evaluation methodologies, uncertainty-aware learning strategies, and computationally efficient architectures for robust real-world autonomous SLAM.

Decarbonizing the building sector requires integrating on-site renewable generation with systematic energy management. Among the most widely adopted alternatives are photovoltaic (PV) systems in buildings; however, they are often implemented as a standalone technological intervention (size–install–estimate savings), without being formally incorporated into an Energy Management System (EnMS) aimed at continuous improvement. In this context, this research addresses this gap through an integrated methodological framework aligned with ISO 50001, in which PV is explicitly included in energy performance management through energy review, the definition of an Energy Baseline (EnB), and the monitoring of Energy Performance Indicators (EnPIs) within the PDCA cycle. The approach articulates the analytical sizing of the PV system based on electricity demand and solar resources; its validation through simulation to ensure operational consistency and a technical, economic, and environmental assessment that translates PV generation into a verifiable reduction in energy imported from the grid and, consequently, into traceable improvements in EnPI under an audit-compatible scheme. The methodology is demonstrated in a multi-family building in Chorrillos, Lima (Peru), where a 14.5 kWp rooftop PV system (25 modules of 580 Wp) is designed to maximize self-consumption during daylight hours. The results show technical performance consistent with the demand profile, economic viability under the conditions of the case, and environmental benefits from replacing grid electricity, along with offsets associated mainly with the manufacture of PV components. The residual gap between the Post-PV EnPIs and the ISO 50001 target confirms that PV integration is a necessary but not sufficient first-cycle action within a comprehensive building decarbonization strategy, with demand-side management and envelope improvements identified as subsequent PDCA cycle priorities. In summary, the central contribution is not the PV sizing itself, but its operational and traceable integration within ISO 50001, making PV a quantifiable, verifiable, and scalable energy improvement action for residential buildings in emerging economies.