Artículos (Organización Industrial y Gestión de Empresas II)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11407

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Integration of the Adaptive Approach in HVAC System Operation: A Case Study
(MDPI, 2025-01) Aparicio Ruiz, Pablo; Ragel Bonilla, Juan Carlos; Barbadilla Martín, Elena; Guadix Martín, José; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP127: Ingeniería de Organización
Although different investigations have been carried out on the analysis of adaptive thermal comfort in naturally ventilated buildings, fewer have focused on mixed mode operation. Moreover, there is limited research as for the implementation of adaptive comfort models into the control system of buildings. Therefore, this paper investigates how the application of a setpoint based on adaptive comfort control (ACC) would affect occupants’ comfort considering mixed mode operation and based on the results of a longitudinal field study in an academic office building of a tertiary educational institution in southern Spain. The manuscript analyses the Thermal Preference Vote over 12 months in a mixed mode room with an HVAC system whose setpoint is adjusted with a previously calculated adaptive algorithm for the building. For that, a thorough analysis was conducted in which users identified situations regarding thermal comfort and the operation of the conditioning system was collected. The results indicate that it is possible to develop adaptive comfort models that ensure the thermal well-being of occupants. Moreover, this study highlights the need for further research to assess the implications of ACC in terms of comfort and energy consumption as well as addressing the future improvements and the limitations of the work carried out.
Investment Strategies to Maintain the State of Water Networks
(2024) Robles-Velasco, Alicia; Aparicio Ruiz, Pablo; Cortés, Pablo; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Junta de Andalucía; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TEP127: Ingeniería de Organización
Purpose: This article focuses on the problem of deciding the annual investment that a water company should allocate to the rehabilitation of its distribution and sanitation networks. The objective is to find the investment amount necessary to maintain an adequate quality and sustainability of the infrastructure. It is not a simple decision, as there are different criteria that may be of interest to the different agents involved. In this paper, we consider four criteria related to the reliability of individual pipes and the complete network. These indicators are the infrastructure value index, the average age of network pipes, the risk index and the probability of failure. Design/methodology/approach: A methodology is proposed to estimate the best annual investment by analysing the evolution of these indicators. Concretely, two strategies are tested. The first one is a minimax-based approach that seeks a balanced solution for all the indicators. The second one is named as minimal deviation strategy and seeks to minimise the deviation of all the indicators in the last year of the time horizon compared to the initial year. Findings: In order to obtain a realistic sample of the performance of both strategies, 201 scenarios, i.e. 201 different annual investments have been simulated. According to the first strategy, an annual investment of 55.5 M€ is the best option, while the minimal deviation strategy presents an annual investment of 39.5 M€ as the best decision. The study reveals that different evaluation functions lead to completely different annual investment. Concretely, the minimax evaluation function is more conservative than the minimal deviation strategy. Originality/value: This study proposes an original approach to address the decision problem of investments in asset management. To the best of the authors’ knowledge, it is the first attempt to treat that problem using this kind of evaluation functions. However, it is still a relatively straightforward proposal and there are many possible options to continue this line of research.
A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon
(Elsevier, 2024-11) Cortés, Pablo; Escudero Santana, Alejandro; Barbadilla Martín, Elena; Guadix Martín, José; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia, Innovación y Universidad (MICIU). España; Universidad de Sevilla. TEP127: Ingeniería de Organización
The recent advancements in energy production, storage, and distribution are creating unprecedented opportunities in the field. Major consumers can benefit from the implementation of distributed energy resource networks capable of generating electricity or heating from sources, often renewable ones, in close proximity to the point of use, rather than relying on centralized generation sources from power plants. In this paper, we introduce a pioneering model designed to determine the optimal set of energy commands in a distributed energy resource network, minimizing operational costs in a time horizon. Indeed, we propose an innovative mixed-integer linear programming formulation rooted in the production-inventory models commonly employed in aggregate production planning. The system integrates diverse energy generation sources, storage facilities, and demand points, encompassing both electric and heating commodities. The optimum of the model is achieved for all analyzed instances of the test library (2 scenarios-20 instances) in an exceptionally short time, outperforming other approaches previously presented in the literature. We employed the Gurobi optimizer to solve the model, obtaining rapid responses that ensure real-time decision-making and facilitate effective control of the distributed energy resource network within a three-days’ rolling horizon, as discussed in a simulated real-life application case study. Indeed, the proposed model solves in less than 1 s, enabling near-instantaneous decision-making. This swift solution time surpasses any known references in the field, effectively shifting the bottleneck in DER network operation from the decision-making process to the forecasting of demand and weather conditions. While forecasting typically requires a minimum of 15 min, our approach suggests that a reduction in this forecasting time could further enhance the control system's response time, given the model's ability to deliver optimal solutions almost immediately. The real-time availability of optimal solutions allows for the seamless incorporation of stochastic elements into the control loop via a rolling horizon process.
A predictive framework for last-mile delivery routes considering couriers’ behavior heterogeneity
(Elsevier, 2024-12) Pegado Bardayo, Ana; Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia, Innovación y Universidades (MICINN). España
Last-mile route prediction is a powerful tool for freight delivery companies that can be essential in the development of new features such as arrival time prediction or accurate workload allocation. Existing methodologies propose prediction models that, on the one hand, require external information, such as traffic data or a prior division of work areas, and, on the other hand, assume courier homogeneity in the search for travel patterns. However, this assumption may introduce noise in the predictions since different couriers may be subject to different routing habits. This study proposes a comprehensive predictive framework for delivery routes, which allows identifying the routing profiles that best suit each courier and work area, and which starts from basic information available for any carrier, ensuring the scalability of the tool. The analysis is supported by a case study, where the results obtained bring to light the heterogeneity in the routing decisions of the different drivers and show that the proposed approach produces consistent and accurate predictions for the vast majority of them.
Near-optimal operation of the distributed energy resources in a smart microgrid district
(Elsevier, 2020-04) Cortés, Pablo; Auladell León, Paloma; Muñuzuri, Jesús; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Economía y Competitividad (MINECO). España
This paper considers the case study of a smart microgrid district at Graciosa Island in the Canary Islands. The smart energy microgrid district consists of several households and a public use building (school) that includes renewable energy sources (photovoltaic), Li-ion batteries for electric energy storage, domestic hot water heaters acting as thermal energy storage, a pool for balancing energy consumption and supplies, and the connection to the electric grid. We have modelled such a problem as a nonlinear mathematical programming model that is linearly approximated using special ordered sets of type 2. The linear approximation is solved using Gurobi optimization software, providing close-to-optimum solutions within an interval of 15 min that allows near-real-time operation of the smart energy district. The obtained results allow advancement of the net-zero energy neighbourhood concept in all the evaluated scenarios within a daily horizon and a positive energy balance in wider horizons. Obviously, these results are obtained in part due to the magnificent insolation conditions of the Canary Islands, but they allow justifying that the appropriate use of renewable energy resources and energy storage systems together with a balancing mechanism at the district level (such as the pool in our case study) may also lead to near-net-zero energy neighbourhoods in other geographical locations.
Using IoT data and applications to improve port-based intermodal supply chains
(Elsevier, 2020-01) Muñuzuri, Jesús; Onieva, Luis; Cortés, Pablo; Guadix Martín, José; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Fundación FIUS; Ministerio de Economía y Competitividad (MINECO). España
The complexity of port terminal operations and intermodal transshipments has pushed the introduction of information and communication technologies (ICT) to assist the multiple stakeholders involved in the multiple decision-making processes. More recently the development of systems based on the Internet of Things (IoT) represents a further step in the automation of data acquisition and processing. We present here an IoT system designed to optimize, manage and monitor container transport operations along an intermodal corridor, combining rail scheduling and inland vessel navigation. The system has a modular design, enabling the interaction with external systems, independently of their nature, through the cloud-based FIWARE platform. We provide a description of the three inter-related subsystems (container tracking, rail management and inland navigation), and a description of the expected supply chain benefits derived from the implementation of the system, both in terms of shippers and of terminal integration in the logistics flow. This system is currently operative at the Port of Seville, the main transshipment node in the Madrid – Seville – Canary Islands intermodal corridor in Spain.
Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach
(MDPI, 2024-08) Flores-Huamán, Kenny-Jesús; Escudero Santana, Alejandro; Muñoz Díaz, María Luisa; Cortés, Pablo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP127: Ingeniería de Organización
This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.
Exploring the correlation between courier workload, service density and distance with the success of last-mile and first-mile reverse logistics
(Springer, 2024) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Onieva, Luis; Muñoz Díaz, María Luisa; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Agencia Estatal de Investigación. España; Ministerio de Universidades. España; Universidad de Sevilla. TEP127: Ingeniería de Organización
Given the recent surge in online sales, particularly accentuated by the health crisis in 2020 and 2021, companies operating in the retail sector have increasingly recognised the importance of business-to-consumer (B2C) distribution. Consequently, last-mile logistics optimization has garnered increased attention in both academic and industry contexts. In this study, we examine the relationship between the workloads of couriers and their proficiency in executing assigned services in a B2C last-mile and first-mile reverse logistics environment. Additionally, we evaluate the connection between service density in an area and the distance between warehouses and service points with completion rates among couriers. By analysing a dataset corresponding to the deliveries and collections made in Madrid in 2021, we identify significant and moderate correlations between the couriers’ workloads and service completion rate. It should be noted that the correlations of completion rate with distance and visit frequency to each area are weak, yet statistically significant
Double deck elevator group control systems using evolutionary algorithms: interfloor and lunchpeak traffic analysis
(Elsevier, 2021-05) Cortés, Pablo; Muñuzuri, Jesús; Vázquez Ledesma, Alejandro; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
The continuous development of high-rise buildings around the world requires the installation of efficient elevator systems able to vertically transport the different passengers along the buildings in their daily journeys. Double deck elevators can increase the efficiency of these vertical transportation systems. Double deck elevators consist of two adjacent cabins that are joined and travel together along the same shaft, so the handling capacity of the system can be improved by allowing the dispatch of passengers with destination to two consecutive floors at the same instant. This type of architecture emerges as especially appropriate for uppeak traffic conditions. However, its suitability has not been sufficiently analysed for non-dominant (up or down) traffic patterns, such as interfloor and lunchpeak traffic. Our paper deals with conventionally controlled double deck elevators, where the Elevator Group Control System (EGCS) requires specific car-landing call allocation algorithms able to manage such special car architectures. Along this line, we propose a genetic algorithm that demonstrated a good performance when compared to a tabu search algorithm that was used as benchmark for comparison, taking into account different fitness evaluation functions (overall dispatching time and nearest call). The analysis was undertaken for interfloor and lunchpeak traffics and the average waiting, transit and journey times, and the energy consumption are reported as performance indexes of the vertical transportation system. The algorithms produced efficient results outperforming the considered benchmark and emerged as very competitive algorithms considering all the performance indexes as a whole. Results were tested using ELEVATE, the standard simulation software for vertical transportation.
Solving the picker routing problem in multi-block high-level storage systems using metaheuristics
(Springer Nature, 2023-06) Cano, José Alejandro; Cortés, Pablo; Muñuzuri, Jesús; Correa-Espinal, Alexander; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
This study aims to minimize the travel time in multi-block high-level storage systems considering height level constraints for picking devices to leave aisles. Considering these operating environments, the formulation of minimum travel times between each pair of storage positions is proposed and the picker routing problem (PRP) is solved by means of Genetic Algorithms (GA) and Ant Colony Optimization (ACO). A parameter tuning is performed for both metaheuristics, and the performance of the GA and ACO is compared with the optimal solution for small-sized problems demonstrating the reliability of the algorithms solving the PRP. Then, the performance of the GA and ACO is tested under several warehouse configurations and pick-list sizes obtaining that both metaheuristics provide high-quality solutions within short computing times. It is concluded that the GA outperforms the ACO in both efficiency and computing time, so it is recommended to implement the GA to solve the PRP in joint order picking problems.
Scheduling consecutive days off: A case study of maritime pilots
(Elsevier, 2021-05) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Onieva, Luis; Cortés, Pablo; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Ministerio de Economía y Competitividad (MINECO). España; Universidad de Sevilla. TEP127: Ingeniería de Organización
Pilots are essential for the operation of maritime ports and an efficient piloting workforce management is critical to provide quality service to incoming vessels, to comply with the strict labor regulations associated with piloting and to avoid penalties due to delays in service. However, designing labor schedules that meet workforce demand and fulfill both labor requirements and workers’ preferences at once can become an arduous task. This paper presents two general days-on and days-off scheduling mixed integer linear programming models, which aim to configure extended breaks for each staff member. The first model produces schedules with two long breaks of bounded durations for each worker and minimizes the difference between the employees’ workloads. Having the option to modify the minimum lengths of each of the two types of breaks allows managers to comply with the workers’ desired rest patterns, while at the same time exploiting the flexibility gained by constraining the off-periods with a lower bound and achieving fair schedules in terms of break lengths and workloads. On the other hand, the second model assigns breaks as extended as possible and minimizes the difference between the rest accumulated by the workers. Its novel formulation allows maximizing the length of the workers’ breaks, an objective rarely found in the literature, and can be adjusted to prioritize the overall duration of the off-periods or the fairness of the distribution of breaks. Results of the application of these models to the piloting workforce in a Spanish port are shown, as well as a sensitivity analysis performed in order to assess the behaviour of the models when dealing with longer planning horizons and greater workforce sizes. Additionally, an ad-hoc model is developed for the assignment of special-maneuvering turns to the pilots.
Single station MILP scheduling in discrete and continuous time
(Springer Link, 2024) Muñoz Díaz, María Luisa; Escudero Santana, Alejandro; Lorenzo Espejo, Antonio; Roel, Leus; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Agencia de Innovación y Desarrollo de Andalucía (IDEA); Universidad de Sevilla; Universidad de Sevilla. TEP127: Ingeniería de Organización
This article focuses on production planning in the metallurgical sector. This study undertakes a detailed comparative study of mixed-integer linear programming models using different time representations: continuous and discrete. The analysis shows that the continuous model consistently outperforms its discrete counterpart in all evaluated scenarios. The key difference between the continuous and discrete models is the continuous model’s ability to deliver better makespan results, achieving an improvement of up to 15% compared to the discrete model. This advantage holds even in complex environments with a high number of tasks and machines, where the continuous model consistently outperforms the discrete model by over 6% in the scenario with the highest number of tasks and machines. This preference extends beyond makespan considerations. The continuous model also maintains an edge in terms of runtime efficiency, achieving better times with a 99% improvement over the discrete model in all scenarios except one. These findings provide concrete evidence for the use of continuous models, which promise more effective production planning in analogous manufacturing domains.
Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
(Springer Link, 2024-05) Aparicio Ruiz, Pablo; Barbadilla Martín, Elena; Guadix Martín, José; Muñuzuri, Jesús; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla; Universidad de Sevilla. TEP127: Ingeniería de la Organización
Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.
A hybrid knowledge-based method for pipe renewal planning in Water Distribution Systems with limited data: Application to Iran
(Elsevier, 2022-10) Salehi, Sattar; Robles-Velasco, Alicia; Seyedzadeh, Ali; Ghazali, Aliakbar; Davoudiseresht, Mohsen; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. TEP127: Ingeniería de Organización
This study uses a hybrid knowledge-based method for pipe renewal planning in Water Distribution Systems (WDSs), which is useful when data are limited. The method is applied to eight Iranian WDSs to demonstrate its effectiveness. An evolutionary systems design negotiation method was used to identify planning criteria for pipe renewal by a 17-member team of expert planners. A group of 48 experienced system operators then ranked the criteria by a nominal group technique. The results indicate when accurate operational data are limited, it is possible to use the combined expertise of knowledgeable planners and experienced operators for planning pipe renewal.
Estimation of a logistic regression model by a genetic algorithm to predict pipe failures in sewer networks
(Springer, 2021-09) Robles-Velasco, Alicia; Cortés, Pablo; Muñuzuri, Jesús; Onieva, Luis; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla; Universidad de Sevilla. TEP127: Ingeniería de Organización
Sewer networks are mainly composed of pipelines which are in charge of transporting sewage and rainwater to wastewater treatment plants. A failure in a sewer pipe has many negative consequences, such as accidents, flooding, pollution or extra costs. Machine learning arises as a very powerful tool to predict these incidents when the amount of available data is large enough. In this study, a real-coded genetic algorithm is implemented to estimate the optimal weights of a logistic regression model whose objective is to forecast pipe failures in wastewater networks. The goal is to create an autonomous and independent predictive system able to support the decisions about pipe replacement plans of companies. From the data processing to the validation of the model, all stages for the implementation of the machine-learning system are explored and carefully explained. Moreover, the methodology is applied to a real sewer network of a Spanish city to check its performance. Results demonstrate that by annually replacing 4% of pipe segments, those whose estimated failure probability is higher than 0.75, almost 30% of unexpected pipe failures are prevented. Furthermore, the analysis of the estimated weights of the logistic regression model reveals some weaknesses of the network as well as the influence of the features in the pipe failures. For instance, the predisposition of vitrified clay pipes to fail and of that pipes with smaller diameters.
Prediction of pipe failures in water supply networks for longer time periods through multi-label classification
(Elsevier, 2023-03) Robles-Velasco, Alicia; Cortés, Pablo; Muñuzuri, Jesús; De Baets, Bernard; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Consejería de Economía, Conocimiento, Empresas y Universidad (Junta de Andalucía); European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TEP127: Ingeniería de Organización
The unexpected failure of pipes is a problem that is hitting the water networks of many cities around the world. Nowadays, many proposals based on the use of machine learning techniques are emerging to combat this problem. However, most studies focus their efforts on predicting failures in short time periods, usually a year, while longer time period predictions would be more valuable to address strategic decisions.In this study, the use of multi-label classification techniques is proposed to simultaneously predict pipe failures in water supply systems for multiple years. For this purpose, three models (discriminant analysis, logistic regression and random forest) and different prediction time periods (one, two and three years) have been analysed. As multi-label data require specific quality metrics and sampling techniques, part of this work is dedicated to their exploration and discussion.The models are evaluated on a real-world seven-year database, achieving successful results. An insightful analysis of the use of the methodology shows how the percentage of avoided pipe failures increases over time. In fact, it is demonstrated that 30.2%, 51.4% and 54.0% of the pipe failures of three consecutive years are avoided according to data from a real network.
Assessment of thermal comfort and energy savings in a field study on adaptive comfort with application for mixed mode offices
(Elsevier, 2018-05-15) Barbadilla Martín, Elena; Guadix Martín, José; Salmerón Lissén, José Manuel; Sánchez Ramos, José; Álvarez Domínguez, Servando; salmerón liss; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Ingeniería Energética; Andalusia Government - "Excellence Projects" P11-TEP-7247; Universidad de Sevilla. TEP127: Ingeniería de Organización; Universidad de Sevilla. TEP143: Termotecnia
The study of the thermal comfort of the occupants of a building represents an important challenge, due to its close relation with energy efficiency. Facing the application of set-point temperatures, the adaptive comfort model proposes the linking of the comfort temperature to the outdoor temperature which would potentially reduce the use of the HVAC system. Although there are studies that propose experimental adaptive models, few verify their effectiveness. In the current study an adaptive comfort algorithm for hybrid buildings is experimentally validated based on a 17-month field study in office buildings in Spain. The implementation of the algorithm in the HVAC control system, both during the cooling and the heating period, allowed for the evaluation of the energy consumption, obtaining savings of 27.5% and 11.4% respectively. The percentage of thermal sensation votes in comfort evolved from 94% (prior to implementing the comfort algorithm) to 87.5% (once implemented) for the summer season and from 79.5% to 81.6% for the winter season. The results demonstrate that the adaptive model is effective for the optimization of HVAC systems, and that it is possible to achieve energy savings without impairing the comfort of its occupants for the type of climate and buildings considered.
Building automation system with adaptive comfort in mixed mode buildings
(Elsevier, 2018-11) Aparicio Ruiz, Pablo; Barbadilla Martín, Elena; Salmerón Lissén, José Manuel; Guadix Martín, José; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Ingeniería Energética; Ministerio de Economía. España; Junta de Andalucía, Consejería de Economía, Innovación, Ciencia y Empleo; Universidad de Sevilla. TEP127: Ingeniería de Organización; Universidad de Sevilla. TEP143: Termotecnia
Although there are many fieldstudies to achieve a model of comfort in free running buildings, fewer studies focus on mixed-mode buildings. Moreover, there are even fewer examples of implementing such algorithms into a building automation system for testing its real validity. In this study, a methodology for implementing and validating an Adaptive Control Algorithm in mixed mode buildings is proposed. In particular, the paper shows the implantation and application of an experimental adaptive control algorithm in the current installation of an office building and without additional costs or specific hardware. The experiment seeks to find a relationship between comfort of their occupants and with energy efficiency. The implementation into the building´s system shows the real applicability and the effectiveness of the adaptive model to hybrid buildings, highlighting that the methodology proposed could be applied in another type of building. The results show that it is possible to improve the energy efficiency, while maintaining the comfort of the users using only the tools yet available in the Building Automation System of the buildings and without additional systems, no extra costs and minimum intervention in its control system
Climatic applicability of downdraught evaporative cooling in the United States of America
(Elsevier, 2018-05) Aparicio Ruiz, Pablo; Schiano-Phan, Rosa; Salmerón Lissén, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Universidad de Sevilla. Departamento de Ingeniería Energética; Universidad de Sevilla. TEP127: Ingeniería de Organización; Universidad de Sevilla. TEP143: Termotecnia
The potential for application of downdraught cooling in the United States of America (U.S.) depends on its climatic characteristics. However, due to the large geographic span of the country, it varies due to differences in latitude, and a range of geographic features influencing climate, including altitude, topography and terrain. This study describes the development of climatic applicability maps of downdraught cooling in the U.S., which can aid designers in the initial identification of the correct cooling strategy for the geographic area of interest. The proposed approach is based on a set of maps, which are derived from two related climatic indexes: dry bulb temperature to wet bulb temperature depression (DBT−WBT), representing the climatic opportunity, and 26 °C minus wet bulb temperature (26 °C−WBT), representing the climatic opportunity against the theoretical cooling requirement for each location. The downdraught cooling strategy and degree of applicability is classified in the map, based on the aforementioned climatic and cooling parameters. Finally, four representative buildings in four different regions with different climatic conditions were selected for climatic analysis. This resulted in the identification of some climate zones for downdraught cooling application in the U.S. and the suggestion of appropriate design strategies for each of them
Analysis of Variables Affecting Indoor Thermal Comfort in Mediterranean Climates Using Machine Learning
(MDPI, 2023-08) Aparicio Ruiz, Pablo; Barbadilla Martín, Elena; Guadix Martín, José; Nevado, Julio; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas II; Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía, grant number US-1380581; Universidad de Sevilla. TEP127: Ingeniería de Organización
To improve the energy efficiency and performance of buildings, it is essential to understand the factors that influence indoor thermal comfort. Through an extensive analysis of various variables, actions can be developed to enhance the thermal sensation of the occupants, promoting sustainability and economic benefits in conditioning systems. This study identifies eight key variables: indoor air temperature, mean radiant temperature, indoor globe temperature, CO2, age, outdoor temperature, indoor humidity, and the running mean temperature, which are relevant for predicting thermal comfort in Mediterranean office buildings. The proposed methodology effectively analyses the relevance of these variables, using five techniques and two different databases, Mediterranean climate buildings published by ASHRAE and a study conducted in Seville, Spain. The results indicate that the extended database to 21 variables improves the quality of the metrics by 5%, underscoring the importance of a comprehensive approach in the analysis. Among the evaluated techniques, random forest emerges as the most successful, offering superior performance in terms of accuracy and other metrics, and this method is highlighted as a technique that can be used to assist in the design and operation or control of a building’s conditioning system or in tools that recommend adaptive measures to improve thermal comfort.

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