Nowadays, reducing energy consumption is the fastest way to reduce the use of fossil fuels and, therefore, greenhouse gas emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are used to maintain an indoor environment in comfortable conditions for its occupants. The combination of these two factors, energy efficiency and comfort, is a considerable challenge for building operations. This paper introduces a design approach to control an HVAC, focused on an energy consumption reduction in the operation of the HVAC system of a building. The architecture was developed using a Raspberry Pi as a coordinator node and wireless connection with sensor nodes for environmental variables and electrical measurement nodes. The data received by the coordinator node is sent to the cloud for storage and further processing. The control system manages the setpoint of the HVAC equipment, as well as the turning on and off the HVAC compressor using an XBee-based solid state relay. The HVAC temperature control system is based on the Predicted Mean Vote (PMV) index calculation, which is used by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) to find the appropriate setpoint to meet the thermal comfort of 80% of users. This method combines the values of humidity and temperature to define comfort zones. The coordinator node makes the compressor control decisions depending on the value obtained in the PMV index. The proposed PMV-based temperature control system for the HVAC equipment achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment, when operating with the same setpoint of 26.5 grades centigrade.

The increasing complexity and automation inherent in the contemporary Industry 4.0 paradigm necessitate robust and proactive fault detection methodologies to ensure both operational efficiency and safety. Existing unsupervised anomaly detection techniques, however, often encounter challenges when confronted with the high dimensionality, inherent noise, and complex interdependencies characteristic of industrial data. This paper proposes a novel unsupervised anomaly detection framework explicitly designed for early fault detection within such complex industrial environments. The proposed data-driven methodology systematically identifies the most effective unsupervised model for anomaly prediction from a candidate set of learning algorithms. This approach is particularly advantageous as it obviates the need for labeled historical fault data, a resource often limited in real-world operational settings. The 2015 PHM Data Challenge dataset, specifically selected for its inclusion of systems exhibiting incomplete fault logs, is used to validate the efficacy of the proposed framework. Findings underscore the significant potential of data-driven methodologies to enhance fault detection capabilities, thereby enabling timely intervention and contributing to the improvement of both the reliability and safety of industrial systems.

Global warming and climate change effects have been of such impact that several countries around the world are enforcing public policies to mitigate them. Mexico has shown a strong commitment to the environment and rational use of energy, as signed on the General Law for Climate Change (GLCC) and stating, in its second article, the goal of a 30% reduction of greenhouse gases by 2020. To add to this goal, the Hermosillo Institute of Technology is implementing a pilot energy saving program that mixes retrofitting of academic buildings and the implementation of automatic controls for lighting and heating, ventilating, and air conditioning (HVAC). The retrofitting is performed by replacing fluorescent T8 tubes with high efficiency LED T8 tubes in a new arrangement. To increase the energy saving obtained by the retrofitting, a building automation and control system (BACS) has been developed and installed. The BACS is implemented using two different networks, the first one communicates a central control unit with the building control node using a private Ethernet network. Inside the building, the control actions are transmitted using a ZigBee network. The energy savings have been estimated as 4864 kWh/year, representing a 36.42% saving, the environmental and health effects are calculated using emission parameters of the nearest power plant to our site, and the procedure presented in Harvard’s Six Cities Study by Dockery. Results show a total CO₂eq equivalent to 0.000409% of the national goal. The economic impacts of the carbon social cost and health benefits are $745.26 USD/year and $4017.71 USD/year while the direct billing savings are $3700.56 USD/year, and these results are based on only one building of the campus.

This paper shows the development of a decision tree for the classification of loads in a non-intrusive load monitoring (NILM) system implemented in a simple board computer (Raspberry Pi 3). The decision tree uses the total energy value of the power signal of an equipment, which is generated using a discrete wavelet transform and Parseval’s theorem. The power consumption data of different types of equipment were obtained from a public access database for NILM applications. The best split point for the design of the decision tree was determined using the weighted average Gini index. The tree was validated using loads available in the same public access database.

This paper presents a comparison of energy consumption and average lighting level between two different lighting systems of an Academic Building, firstly, the existing lighting system using T8 tube fluorescent lamps, and secondly, a proposed lighting system with T8 tube LED lamps. The main objective of this comparison is to find the power consumption and lighting level difference between fluorescent and LED tube lamps used in the same space, as in this case, a classroom. A second objective is to measure the lighting level inside the classroom to make sure that the existing lighting standard in Mexico is been satisfied. The measurements were performed using a power analyzer and a light meter to evaluate the actual power consumption and lighting levels using T8 tube fluorescent lamps, and the proposed retrofitted T8 tube LED lamp system. Lighting is the most important load in offices and classrooms buildings, for that reason, this kind of load has been studied by a lot of researchers. This comparison will provide information about the energy consumption and lighting level of a classroom, helping to know if the classroom accomplishes with the legal Mexican lighting standard. Additionally, will generate information about the energy savings obtained by the LED lighting system over the existing fluorescent lighting system.

This paper presents a brief overview of a simulation of a retrofitting of the lighting system of an Academic Building for energy savings using Rhinoceros and Energy Plus software. The proposed simulations have the main objective of finding the correct distribution and amount of lamps to be installed in a classroom, taking into account the existing standard in Mexico. Using Energy Plus software a set of simulations were performed to evaluate the energy consumption of the actual lighting system, and the LED-based retrofitted lighting system. Lighting is the second important load in offices and/or classroom buildings. The proposed simulations provide information of the amount of lamps needed to accomplish with the legal normative and the number of energy savings obtained with the replace of the fluorescent lamps by LED lamps.