The current difficulty in obtaining financial resources to acquire equipment for training personnel and the requirement of well-trained engineers in the industry looking toward sustainability, creates an opportunity to innovate in teaching tools, such as the proposed in this article, where a virtual world is projected, integrating the planning and design of a photovoltaic power plant (PV). The user can interact while immersed in the virtual scene as if the PV system were already installed, illustrating technical characteristics of selected equipment and its installation details, while the user is interacting with didactic activities focused on visual, auditory, and kinesthetic learning. This paper summarizes an initiative within the teaching–learning context, which aims to show the advantages of using modern tools, such as virtual reality, to achieve teaching goals in a renewable energy course.

This work proposes a real-time electricity bill for quantifying the energy used in domestic facilities in Mexico. This bill is a low-cost tool that takes advantage of the IoT technology for generating an easy reading real-time bill allowing the customers to constantly review and administrate their energy consumption. Using low-cost sensors and the electronic board Particle^® Photon, an energy meter is proposed. The presented prototype is extremely compact and satisfies safety measures to be used by anyone in a domestic installation. The measurement data is displayed and processed in real-time, and an appropriate algorithm determines the accumulated kWh. The energy consumed is displayed using an Html interface of easy interpretation for the customers, given recommendations about their consumption habits and some alarms in case of abnormal or high consumption. As a reinforcement measure for avoiding large consumption bills, the system is programmed to send messages to the user, remembering if the estimated consumption is large.

In this study, a low-cost proposed platform for training dynamics (PPTD) is proposed based on operational amplifiers to understand the dynamics and variables of the agricultural tractor John Deere tractor model 4430 to gain autonomy and analyze the behavior of control algorithms proposed in real time by state feedback. The proposed platform uses commercial sensors and interacts with the Arduino Uno and/or Daq-6009 board from National Instruments. A mobile application (APP) was also developed for real-time monitoring of autonomous control signals, the local reference system, and physical and dynamic variables in the tractor; this platform can be used as a mobile alternative applied to a tractor in physically installed form. In the presented case, the PPTD was mounted on a John Deere tractor to test its behavior; moreover, it may be used on other tractor models similarly as established here. The established results of this platform were compared with models established in MATLAB, validating the proposal. All simulations and developments are shared through a web-link as open-source files so that anyone with basic knowledge of electronics and modeling of vehicles can reproduce the proposed platform.

This paper presents a comprehensive holistic methodology implemented for sustainable lighting systems in educational institutions. The proposed methodology is aligned with the Sustainable Development Goals (SDGs), particularly with SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), and it follows international standards. The six-step process includes viability analysis, project design simulation using DIALux 4.13 software, the installation of LED lighting systems, and the redesign of some electrical circuits, followed by an analysis of return on investment and the monitorization of CO₂ and energy consumption. The proposed methodology results in significant return on investment (ROI), primarily achieved through energy savings and reduced maintenance costs. The implementation of LED tubes, combined with occupancy and natural light sensors, leads to a 66% reduction in energy consumption and a reduction of 15.63 tons (metric tons) of CO₂ annually, translating into a quick payback period of approximately 2.36 years. Additionally, the system includes Long-Term Monitoring, which ensures that energy consumption and lighting levels are continuously tracked.