Cristian Cuji

Climate change is one of the greatest global challenges of the 21st century, primarily driven by carbon dioxide (CO2) emissions resulting from the combustion of fossil fuels. The need to reduce these emissions has propelled the development and adoption of cleaner and more sustainable technologies, among which electric vehicles (EVs) stand out. These vehicles eliminate direct pollutant emissions by not relying on fossil fuels; however, their true environmental impact depends on the efficiency with which their energy storage systems—batteries—are managed [1], [2]. Various studies have explored the use of PID controllers to optimize the battery charging process in EVs. According to [2], [3], [4], the implementation of PID controllers allows precise control of the charging current, minimizing spikes and preventing overcharging that could affect battery lifespan. Furthermore, recent studies [2], [3], [4] demonstrate that PID-based optimization reduces thermal losses by 15% compared to conventional methods, thereby improving energy efficiency and decreasing the indirect carbon footprint. This is particularly relevant since, in many countries, electricity generation still partially relies on non-renewable sources [5]. Moreover, this focus on sustainability aligns with the Sustainable Development Goals (SDGs), specifically in the areas of Affordable and Clean Energy (SDG 7), Industry, Innovation, and Infrastructure (SDG 9), and Climate Action (SDG 13). The implementation of PID controllers not only optimizes the charging process but also directly contributes to emission reductions and the efficient use of energy resources [2], [6]. Compared to other control methods, such as predictive control or fuzzy systems, the PID controller stands out for its simplicity and ease of implementation, while maintaining robust and stable performance [2], [3], [4]. These characteristics reinforce its selection for optimizing charging systems in electric vehicles. Thus, this study proposes the optimized use of PID controllers to maximize energy efficiency and reduce thermal losses during the battery charging process in electric vehicles. Through mathematical analysis and computational simulations, the dynamic behavior of the controlled system is evaluated, identifying improvements in charging stability, a reduction in carbon footprint, and an increase in battery lifespan. The results validate the proposal as an effective and sustainable solution for electric mobility.

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO₂ emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation.

Smart lighting is becoming a strategic component of intelligent and low-carbon urban infrastructure because it combines efficient illumination with connectivity, remote management, and continuous operational monitoring. This study proposes a reproducible unit-level methodological framework that transforms field records from a functional smart-lighting installation into traceable indicators of electrical performance, energy efficiency, avoided emissions, preliminary economic benefit, sensitivity, and conditional scalability. The approach treats the luminaire not only as an electrical load, but as a monitored urban energy node whose operation can be validated, characterized, and compared under planning-oriented control scenarios. The methodology integrates data preprocessing, electrical consistency assessment, representative baseline definition, scenario-based energy modeling, explicit environmental conversion, and conditional scaling to homogeneous lighting assets. The results reveal a stable electrical operating regime and show that managed operating conditions can generate sustained reductions in energy use and associated environmental impacts while preserving analytical transparency between measured variables and scenario-derived indicators. Sensitivity and multivariable analyses further support the robustness of the unit-level interpretation and highlight the value of monitored lighting data for comparative decision-making. The framework therefore provides a technically grounded reference for smart-city lighting management, energy planning, and scalable infrastructure assessment, with relevance to the objectives of SDG 7, SDG 11, and SDG 13. Overall, the study contributes an original data-driven perspective for integrating IoT-enabled lighting, remote supervision, and sustainability-oriented urban management within a common analytical structure.

Smart public lighting systems are increasingly evolving from energy-efficiency technologies into distributed urban data infrastructures. This study examines an IoT-based smart public lighting pilot implemented on a university campus in southern Quito, Ecuador, with the objective of quantifying its informational capacity, storage requirements, and scalability potential. The analysis was based on 11 smart luminaires and 49 days of real operational data. Measurements were recorded every 10 min, considering 10 variables per record, including measured consumption, reference consumption, active power, accumulated energy, operating hours, operational status, events, faults or alarms, energy savings, and avoided CO₂ emissions. The results show that each luminaire can generate 1440 data values per day. Under the same acquisition configuration, a network of 10,000 luminaires would generate approximately 5.256 billion data values per year, while a network of 100,000 luminaires would generate approximately 52.56 billion data values per year, requiring an estimated 1051.2 GB/year of storage. These values are interpreted as scalability scenarios rather than definitive city-wide predictions, given the limited scale and observation period of the pilot. As a methodological contribution, the study proposes the Smart Lighting Informational Capacity Index (SLICI) to estimate the daily data intensity generated by smart lighting networks. The findings demonstrate that the expansion of smart public lighting requires not only efficient luminaires, but also robust digital architectures for data storage, processing, interoperability, cybersecurity, and analytics. The study positions smart public lighting as a strategic platform for urban energy management, predictive maintenance, and data-driven sustainable planning, while highlighting the need for future validation over longer periods and across different urban typologies.

Indoor lighting systems are a significant contributor to building energy consumption while also directly affecting occupant comfort and circadian regulation. Recent advances in smart lighting have introduced adaptive and human-centric approaches; however, the integration of optimization-oriented control strategies with interoperable automation frameworks remains only partially articulated in the literature. This study presents a systematic bibliometric review of smart indoor lighting research, with particular attention to the roles of hyper-heuristics (HH), Internet of Things (IoT) platforms, and IFTTT/Event–Condition–Action (ECA) automation. A PRISMA-based methodology was applied across Scopus, Web of Science, and IEEE Xplore for the period 2010–2025. A total of 5529 records were identified, with 5229 screened after duplicate removal, and 27 core studies included following eligibility assessment. To reduce the risk of over-interpreting null intersections, the review also incorporated a search-sensitivity analysis based on expanded query formulations and title–abstract screening. Bibliometric analysis was conducted using MATLAB and VOSviewer to identify publication trends, technological clusters, and patterns of fragmentation across the literature. The results indicate rapid growth in IoT-based and energy-aware lighting systems, alongside mature research in circadian and comfort-driven lighting. However, explicit indexed evidence connecting hyper-heuristics with IoT platforms and IFTTT/ECA frameworks remains sparse and fragmented in the available literature. Co-occurrence analysis further reveals weak metadata-level connections between optimization techniques and IoT protocols, while the sensitivity analysis confirms that broadened retrieval improves recall but still yields only limited directly relevant evidence. Overall, the review identifies a gap in the explicit convergence of optimization, interoperable IoT infrastructure, and event-driven automation for human-centric indoor lighting. On this basis, it outlines a conceptual integration framework combining hyper-heuristics, IoT middleware, and event-driven control. The findings provide a structured roadmap for future research and implementation-oriented studies aimed at improving both energy efficiency and human-centric comfort in smart indoor environments.

This study compares uniform and stratified sampling strategies applied to hourly solar irradiance signals. The analysis examines how each approach affects signal reconstruction, anomaly detection, and dynamic PV modelling. Using PCHIP interpolation and error metrics such as RMSE and MAE, results show that uniform sampling yields lower global reconstruction error (26.64 W/m² vs. 32.98 W/m²), while stratified sampling captures instantaneous peaks more accurately under high-variability conditions. Stratified sampling also improves anomaly identification due to its more representative temporal distribution. These findings highlight a practical trade-off between minimizing average error and preserving extreme events, providing guidance for PV estimation, forecasting, and discrete-time control applications.