Browsing by Subject "Energy efficiency"

Now showing 1 - 9 of 9
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    A methodology for energy multivariate time series forecasting in smart buildings based on feature selection
    (Elsevier, 2019-05-10) González Vidal, Aurora; Jiménez Barrionuevo, Fernando; Skarmeta Gómez, Antonio; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the created features that have a potential impact on the information that can be extracted through the machine learning process. The mining of knowledge related to a concept is done on the basis of the features of data. The process of finding the best combination of features is called feature selection. In this paper we deal with multivariate time-dependent series of data points for energy forecasting in smart buildings. We propose a methodology to transform the time-dependent database into a structure that standard machine learning algorithms can process, and then, apply different types of feature selection methods for regression tasks. We used Weka for the tasks of database transformation, feature selection, regression, statistical test and forecasting. The proposed methodology improves MAE by 59.97% and RMSE by 40.75%, evaluated on training data, and it improves MAE by 42.28% and RMSE by 36.62%, evaluated on test data, on average for 1-step-ahead, 2-step-ahead and 3-step-ahead when compared to not applying any feature selection methodology.
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    Energy and maintenance management systems in the context of industry 4.0. Implementation in a real case
    (Elsevier, 2021-03-01) Alarcón García, Mariano; Martínez García, Fernando Manuel; Gómez de León Hijes, Félix; Ingeniería de la Información y las Comunicaciones
    Industry 4.0 facilities and organization structures are used to improve energy efficiency and maintenance in industry. One of the challenges of Industry 4.0 is the urgent need to lower the consumption of energy, water and raw materials, as well as to ensure the safe and reliable running of a plant and reduce maintenance costs. The work presented herein proposes the low cost integration of Energy Management Systems (EMS) and Maintenance Management Systems (MMS) within the main management systems of a company, formed by organization appliances such as Enterprise Resource Planning (ERP), Distributed Control Systems (DCS) or Manufacturing Execution Systems (MES), The proposal has the virtue of introducing maintenance and energy saving in the company agenda. A central role in such integration is played by the generalized use of network analyzers in electric machinery; the information gained from these network analyzers is not only interesting for ascertaining energy consumption, but also for understanding the real operating conditions of the machinery, identifying premature failures or abnormal behavior. Dramatic reductions (about 50%) in energy consumption and required maintenance inspections, as well as the extension of piece replacement time, are achieved at a reasonable cost. The proposed measures have been implemented by a multinational corporation, owners of a chemical plant located in El Palmar (Murcia-Spain).
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    Open Access
    La Formación Profesional en la familia de Energía y Agua
    (Comunidad Autónoma de la Región de Murcia, Consejería de Educación y Cultura, Servicio de Publicaciones, 2019) Alcaraz Caballero, Antonio José; Carrasco Martínez, Pilar; Montoya Caravaca, Antonio
    La Formación Profesional tiene un papel clave en proporcionar a las personas los conocimientos y las habilidades para insertarse en el mercado laboral y permitirles una carrera exitosa. La evolución tecnológica está demandando nuevas capacitaciones a los trabajadores y está provocando la aparición de nuevos puestos de trabajo. Al mismo tiempo, la lucha contra los efectos del cambio climático requiere un consumo energético, una generación eléctrica y una gestión del agua sostenible. Fomentar la eficiencia energética y las energías renovables es esencial para conseguir estos objetivos. La Formación Profesional preparará a los jóvenes para acceder y participar de manera exitosa y sostenible en este mercado laboral.
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    Implementation of digitalization technologies for optimizing energy efficinency and thermal management in steam industrial processes
    (Elsevier, 2025-04-28) Martínez García, F. M.; Molina García, A.; Alarcón García, Mariano; Gómez de León Hijes, Félix; Sánchez Robles, J.; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Química
    In recent decades, within the framework of the digital world, the control and management of industrial steam consumption and demand have become increasingly difficult. Specifically, in complex industrial processes where spaces and layouts are limited, the possibility of modifying facilities and equipment is significantly less. In addition, the economic cost of controlling consumption per production line or equipment often makes investments unfeasible. Therefore, global consumption is usually controlled in the most complex industrial processes without any subsequent partial demand monitoring. This paper focuses on the development, implementation, and evaluation of a steam control platform by consumers, avoiding the need to install a mass flow meter by equipment, line or installation. An ad-hoc algorithm is designed and assessed by combining information from the monitoring and management systems for optimal operation. Data gathered through combined information from different management systems are integrated into a proposed global management and monitoring platform. In addition, the application of the designed steam control algorithm is used as key information for business decision-making, cost estimation, and predictive maintenance control. A case study and the proposed platform were implemented in an actual chemical plant located in Spain from a multinational corporation. This case study can also be used as a reference model, providing a scalable and easily replicable solution for other factories with steam-consuming equipment. From the results, it is possible to achieve effective steam consumption monitoring for over 50 production units by integrating steam flow meters into the main supply-lines, in combination with other industrial management systems: ERP, MES, or DCS. Notably, this approach is carried out with an investment cost 25 times lower than traditional methods, providing high efficiency, suitability and cost-effectiveness. The results and discussion of the gathered data and the global platform are also included in the paper. The proposed methodology and algorithms are not only suitable for this chemical company case study, but also scalable for any type of factory/plant with steam consumption equipment or process.
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    Multiversioned Decoupled Access-Execute: the Key to Energy-Efficient Compilation of General-Purpose Programs
    (ACM, 2016-03-17) Koukos, Konstantinos; Ekemark, Per; Zacharopoulos, Georgios; Spiliopoulos, Vasileios; Kaxiras, Stefanos; Jimborean, Alexandra; Ingeniería y Tecnología de Computadores
    Computer architecture design faces an era of great challenges in an attempt to simultaneously improve performance and energy efficiency. Previous hardware techniques for energy management become severely limited, and thus, compilers play an essential role in matching the software to the more restricted hardware capabilities. One promising approach is software decoupled access-execute (DAE), in which the compiler transforms the code into coarsegrain phases that are well-matched to the Dynamic Voltage and Frequency Scaling (DVFS) capabilities of the hardware. While this method is proved efficient for statically analyzable codes, generalpurpose applications pose significant challenges due to pointer aliasing, complex control flow and unknown runtime events. We propose a universal compile-time method to decouple generalpurpose applications, using simple but efficient heuristics. Our solutions overcome the challenges of complex code and show that automatic decoupled execution significantly reduces the energy expenditure of irregular or memory-bound applications and even yields slight performance boosts. Overall, our technique achieves over 20% on average energy-delay-product (EDP) improvements (energy over 15% and performance over 5%) across 14 benchmarks from SPEC CPU 2006 and Parboil benchmark suites, with peak EDP improvements surpassing 70%.
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    Políticas y medidas contra la pobreza energética ¿a quién le corresponde?
    (2017-11-01) Raya Diez, Esther; Gómez Pérez, Melchor
    La pobreza energética es una de las múltiples caras de la pobreza. No siempre visible y no suficientemente conocida. Los datos del Consejo Económico y Social son suficientemente contundentes al afirmar que este tipo de pobreza afecta ya a 54 millones de personas en la Unión Europea. España es el cuarto país de la UE con más personas en tal situación, en 2012 se estimaba que ascendía a 7 millones. Y la Organización Mundial de la Salud advierte de los riesgos para la salud de la misma, entre la que se incluyen muertes prematuras o problemas de salud mental entre otros.En el presente trabajo se proponen diferentes medidas de actuación orientadas a la reducción de la pobreza energética. Por un lado se presentan medidas de tipo informativo, y por otro lado medidas de financiación a la inversión en soluciones definitivas, que permiten combatir el problema a medio y largo plazo. Se valora su alcance desde un enfoque de derechos humanos.
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    Providing personalized energy management and awareness services for energy efficiency in smart buildings
    (MDPI, 2017-09-07) Fotopoulou, Eleni; Zafeiropoulos, Anastasios; Simsek, Umutcan; González Vidal, Aurora; Tsiolis, George; Gouvas, Panagiotis; Liapis, Paris; Fensel, Anna; Skarmeta Gómez, Antonio; Terroso Sáenz, Fernando; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.
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    Subsidies for investing in energy efficiency measures: Applying a random forest model for unbalanced samples
    (Elsevier Ltd., 2024-04-01) Álvarez Díez, Susana; Baixauli Soler, Juan Samuel; Lozano Reina, Gabriel; Rodríguez-Linares Rey, Diego; Organización de Empresas y Finanzas; Métodos Cuantitativos para la Economía y la Empresa; Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Organización de Empresas y Finanzas; Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Métodos Cuantitativos para la Economía y la Empresa
    Investing in energy efficiency measures is a major challenge for SMEs, both for environmental and economic reasons. However, certain barriers often make it difficult to invest in such measures. Although public financial support helps to overcome economic barriers, public bodies face the challenge of identifying which SMEs display the greatest potential to invest in energy efficiency measures. By applying a random forest technique and by using sampling balancing techniques, this paper identifies the profile of industrial SMEs that might be potential beneficiaries of public aid, thereby helping public institutions to target their calls and direct their efforts towards this group of SMEs. Specifically, liquidity and indebtedness are found to be the most useful predictors for SMEs in the industrial sector. The results are robust and reveal that applying a random forest approach for unbalanced samples offers greater predictive capacity and statistical power than applying traditional estimation techniques. By identifying potentially benefiting firms, this work helps to boost the effectiveness of public subsidies and to improve the channeling of public funds, which ultimately favors investment in energy efficiency.
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    Wrong-Path-Aware Entangling Instruction Prefetcher
    (Institute of Electrical and Electronics Engineers, 2024) Ros, Alberto; Ingeniería y Tecnología de Computadores
    Instruction prefetching is instrumental for guaranteeing a high flow of instructions through the processor front end for applications whose working set does not fit in the lowerlevel caches. Examples of such applications are server workloads, whose instruction footprints are constantly growing. There are two main techniques to mitigate this problem: fetch directed prefetching (or decoupled front end) and instruction cache (L1I) prefetching. This work extends the state-of-the-art Entangling prefetcher to avoid training during wrong-path execution. Our Entangling wrong-path-aware prefetcher is equipped with microarchitectural techniques that eliminate more than 99% of wrong-path pollution, thus reaching 98.9% of the performance of an ideal wrongpath-aware solution. Next, we propose two microarchitectural optimizations able to further increase performance benefits by 1.8%, on average. All this is achieved with just 304 bytes. Finally, we study the interplay between the L1I prefetcher and a decoupled front end. Our analysis shows that due to pollution caused by wrong-path instructions, the degree of decoupling cannot be increased unlimitedly without negative effects on the energy-delay product (EDP). Furthermore, the closer to ideal is the L1I prefetcher, the less decoupling is required. For example, our Entangling prefetcher reaches an optimal EDP with a decoupling degree of 64 instructions.