Energy Management

Artificial Intelligence in decision making processes involving Energy management and Operations looking to improve the SDGs.

Introduction

Emerging rise in power costs and sustainability of the available energy has prompted industries to seek alternative solutions that could address energy consumption as the second highest contributor to business expenditure compared to salaries and rentals. Regrettably, numerous industries are still reluctant to benefit from the opportunities that exist on energy saving and consciousness, due to inadequate knowledge on energy management, resources and tools to monitor the losses.

Furthermore, the exploration of energy efficiency baseline may benefit the life cycle cost of the overall proposed facility through energy efficient means of production, and continuous improvement practises in big data analytics towards reduction of energy costs significantly through integration of energy efficiency software (EES). To achieve this, a bottom-up approach methodology was adopted, using information on energy cost as a baseline to allow centralisation and cloud hosting of data through a web-based interacting energy efficiency sustainability framework platform, to determine the economic impacts of energy measurement and verification on energy consumption and environment.

Critical power buildings encompass facilities such as hospitals, data centers, airports and essentially any building where the availability and reliability of electricity is crucial and an interruption or diminished quality would lead to serious consequences. As we know from research on IoT investments, buildings and facilities are among the main IoT use cases for several years to come and this also goes for critical power buildings. The automation of airport facilities, for instance, is the IoT use case with the fastest growth in spending until 2021 (33.4%)

In the development of Industry 4.0, energy management and energy efficiency overall has taken center stage. As a matter of fact, knowing the regulations, specific power needs and increasing importance of energy to drive the smart factory and smart industry, there is more attention than ever for energy efficiency in industrial markets.

It is expected that advanced modelling techniques can help in bringing more accurate models, where effectiveness and accountability become critical for decision makers, providing measurable evidences of achievements of SDGs.

PhD candidates can contribute to the development of such advanced models, either to forcast the energy behavior of individual facilities or to check performance of energy plans when pollutants become considered. Indeed, applications to specific fields such as steel making or logistics will be also possible.

Related Publications

Some of the interesting papers produced in this research can be found underneath,

  • [DOI] J. Ordieres-Meré, T. P. Remón, and J. Rubio, “Digitalization: an opportunity for contributing to sustainability from knowledge creation,” Sustainability, vol. 12, iss. 4, p. 1460, 2020.
    [Bibtex] 
    @Article{ordieres2020digitalization,
title = {Digitalization: An opportunity for contributing to sustainability from knowledge creation},
author = {Joaqu\'in Ordieres-Meré and Tomás Prieto Remón and Jesús Rubio},
journal = {Sustainability},
volume = {12},
number = {4},
pages = {1460},
year = {2020},
url = {https://www.mdpi.com/2071-1050/12/4/1460},
doi = {10.3390/su12041460},
publisher = {Multidisciplinary Digital Publishing Institute},
note = {\textbf{Q2}; 2.468; Environmental Science},
gsid = {8913950056194442005},
ncites = {101},
}
  • [DOI] J. Villalba-Díez, M. Molina, J. Ordieres-Meré, S. Sun, D. Schmidt, and W. Wellbrock, “Geometric deep lean learning: deep learning in industry 4.0 cyber–physical complex networks,” Sensors, vol. 20, iss. 3, p. 763, 2020.
    [Bibtex] 
    @Article{villalba2020geometric,
title = {Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber--Physical Complex Networks},
author = {Javier Villalba-Díez and Martin Molina and Joaqu\'in Ordieres-Meré and Shengjing Sun and Daniel Schmidt and Wanja Wellbrock},
journal = {Sensors},
volume = {20},
number = {3},
pages = {763},
year = {2020},
url = {https://www.mdpi.com/1424-8220/20/3/763},
doi = {10.3390/s20030763},
publisher = {Multidisciplinary Digital Publishing Institute},
note = {\textbf{Q1}; 3.275; Instruments \& Instrumentation},
gsid = {15286386535255665589},
ncites = {21},
}
  • [DOI] K. A. G. Arano, S. Sun, J. Ordieres-Mere, and others, “The use of the internet of things for estimating personal pollution exposure,” International journal of environmental research and public health, vol. 16, iss. 17, p. 3130, 2019.
    [Bibtex] 
    @Article{arano2019use,
title = {The Use of the Internet of Things for Estimating Personal Pollution Exposure},
author = {Keith April G Arano and Shengjing Sun and Joaquin Ordieres-Mere and others},
journal = {International journal of environmental research and public health},
volume = {16},
number = {17},
pages = {3130},
year = {2019},
url = {https://www.mdpi.com/1660-4601/16/17/3130},
doi = {10.3390/ijerph16173130},
publisher = {Multidisciplinary Digital Publishing Institute},
note = {\textbf{Q2}; 2.468; Environmental Sciences},
gsid = {10433230395058227202},
ncites = {22},
}
  • F. Boenzi, J. Ordieres-Meré, and R. Iavagnilio, “Life cycle assessment comparison of two refractory brick product systems for ladle lining in secondary steelmaking,” Sustainability, vol. 11, iss. 5, p. 1295, 2019.
    [Bibtex] 
    @Article{boenzi2019life,
title = {Life Cycle Assessment Comparison of Two Refractory Brick Product Systems for Ladle Lining in Secondary Steelmaking},
author = {Francesco Boenzi and Joaqu\'in Ordieres-Meré and Raffaello Iavagnilio},
journal = {Sustainability},
volume = {11},
number = {5},
pages = {1295},
year = {2019},
url = {https://www.mdpi.com/2071-1050/11/5/1295},
publisher = {Multidisciplinary Digital Publishing Institute},
note = {\textbf{Q2}; 2.468; Environmental Science},
gsid = {13117482918847753295},
gsid = {13117482918847753295},
ncites = {3},
ncites = {3},
}
  • [DOI] G. Bing, Z. Xiaochen, G. Qing, and J. Ordieres-Meré, “Discovering the patterns of energy consumption, gdp, and $CO_{2}$ emissions in china using the cluster method,” Energy, vol. 166, p. 1149–1167, 2019.
    [Bibtex] 
    @Article{bing2018discovering,
title = {Discovering the patterns of energy consumption, GDP, and $CO_{2}$ emissions in China using the cluster method},
author = {Gong Bing and Zheng Xiaochen and Guo Qing and Joaqu\'in Ordieres-Meré},
journal = {Energy},
pages = {1149--1167},
volume = {166},
year = {2019},
publisher = {Elsevier},
url = {https://www.sciencedirect.com/science/article/pii/S0360544218321388},
doi = {10.1016/j.energy.2018.10.143},
note = {\textbf{Q1}; 6.082; Energy \& Fuels},
gsid = {6260347305797686885},
ncites = {25},
}
  • [DOI] I. DeSanctis, J. B. Ordieres Mere, M. Bevilacqua, and F. E. Ciarapica, “The moderating effects of corporate and national factors on lean projects barriers: a cross-national study,” Production planning & control, p. 1–20, 2018.
    [Bibtex] 
    @Article{desanctis2018moderating,
title = {The moderating effects of corporate and national factors on lean projects barriers: a cross-national study},
author = {Ilaria DeSanctis and Joaquin Bienvenido {Ordieres Mere} and Maurizio Bevilacqua and Filippo Emanuele Ciarapica},
journal = {Production Planning \& Control},
pages = {1--20},
year = {2018},
publisher = {Taylor \& Francis},
url = {https://www.tandfonline.com/doi/abs/10.1080/09537287.2018.1494345?journalCode=tppc20},
doi = {10.1080/09537287.2018.1494345},
note = {\textbf{Q1}; 3.340; Operation Research \& Management Sciences},
gsid = {1027267136884919214},
ncites = {31},
}
  • [DOI] X. Zheng, M. Wang, and J. B. Ordieres Meré, “Comparison of data preprocessing approaches for applying deep learning to human activity recognition in the context of industry 4.0,” Sensors, vol. 18, iss. 7, p. 2146, 2018.
    [Bibtex] 
    @Article{DBLP:journals/sensors/ZhengWM18,
author = {Xiaochen Zheng and Meiqing Wang and Joaqu\'in B. {Ordieres Meré}},
title = {Comparison of Data Preprocessing Approaches for Applying Deep Learning
to Human Activity Recognition in the Context of Industry 4.0},
journal = {Sensors},
volume = {18},
number = {7},
pages = {2146},
year = {2018},
url = {https://doi.org/10.3390/s18072146},
doi = {10.3390/s18072146},
timestamp = {Thu, 13 Sep 2018 18:11:51 +0200},
biburl = {https://dblp.org/rec/bib/journals/sensors/ZhengWM18},
bibsource = {dblp computer science bibliography, https://dblp.org},
note = {\textbf{Q1}; 3.031; Instruments \& Instrumentation},
gsid = {14060541372438033818},
ncites = {92},
}
  • [DOI] B. Gong and J. Ordieres-Meré, “Reconfiguring existing pollutant monitoring stations by increasing the value of the gathered information,” Environmental modelling & software, vol. 96, pp. 106-122, 2017.
    [Bibtex] 
    @Article{Gong2017106,
title = {Reconfiguring existing pollutant monitoring stations by increasing the value of the gathered information },
journal = {Environmental Modelling \& Software },
volume = {96},
pages = {106 - 122},
year = {2017},
issn = {1364-8152},
doi = {https://doi.org/10.1016/j.envsoft.2017.06.034},
url = {http://www.sciencedirect.com/science/article/pii/S1364815216306466},
author = {Bing Gong and Joaqu\'in Ordieres-Meré},
keywords = {Pollution forecasting},
note = {\textbf{Q1}; 4.177; Computer Science, Interdisciplinary Applications},
gsid = {3534314409908941768},
gsid = {3534314409908941768},
ncites = {3},
ncites = {3},
}
  • [DOI] F. Shrouf, B. Gong, and J. Ordieres-Meré, “Multi-level awareness of energy used in production processes,” Journal of cleaner production, vol. 142, Part 4, pp. 2570-2585, 2017.
    [Bibtex] 
    @Article{Shrouf20172570,
title = {Multi-level awareness of energy used in production processes },
journal = {Journal of Cleaner Production },
volume = {142, Part 4},
pages = {2570 - 2585},
year = {2017},
issn = {0959-6526},
doi = {http://dx.doi.org/10.1016/j.jclepro.2016.11.019},
url = {http://www.sciencedirect.com/science/article/pii/S0959652616318534},
author = {Fadi Shrouf and Bing Gong and Joaquin Ordieres-Meré},
keywords = {Multi-level energy awareness},
comment = {Jmr2JHQAAAAJ:t7zJ5fGR-2UC},
note = {\textbf{Q1}; 5.651; Engineering, Environmental},
gsid = {2215004895783949673},
ncites = {37},
}
  • B. Gong and J. Ordieres-Meré, “Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: case study of hong kong,” Environmental modelling & software, vol. 84, p. 290–303, 2016.
    [Bibtex] 
    @Article{gong2016prediction,
title = {Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong},
author = {Bing Gong and Joaqu\'in Ordieres-Meré},
journal = {Environmental Modelling \& Software},
volume = {84},
pages = {290--303},
year = {2016},
url = {http://www.sciencedirect.com/science/article/pii/S1364815216302602},
publisher = {Elsevier},
comment = {Jmr2JHQAAAAJ:_Ybze24A_UAC},
note = {\textbf{Q1}; 4.404; Computer Science, Interdisciplinary Applications},
gsid = {13312585115650841422},
ncites = {72},
}
  • J. B. O. Meré, “Bigdata e iot: claves del modelo de negocio para la empresa industrial del siglo xxi,” Economía industrial, iss. 392, p. 113–122, 2014.
    [Bibtex] 
    @Article{mere2014bigdata,
title = {BIGDATA e IoT: claves del modelo de negocio para la empresa industrial del siglo XXI},
author = {Joaqu\'in Bienvenido Ordieres Meré},
journal = {Econom\'{i}a industrial},
number = {392},
pages = {113--122},
year = {2014},
url = {http://dialnet.unirioja.es/servlet/articulo?codigo=4754825},
publisher = {Ministerio de Industria, Energ\'{i}a y Turismo},
comment = {Jmr2JHQAAAAJ:NhqRSupF_l8C},
gsid = {9244128200398958313},
ncites = {7},
}

Related Theses

Indeed, it is possible to make reference to specific thesis already finished, which are related to this research line

  • X. Zheng, “Relevant framework for social applications of internet of things by means of machine learning.,” Tesis PhD Thesis, 2019.
    [Bibtex] 
    @PHDTHESIS{upm20190429,
title = {Relevant framework for social applications of internet of things by means of machine learning.},
school = {Industrial Engieering. Universidad Polit\'ecnica de Madrid},
author = {Xiaochen Zheng},
year = {2019},
type = {Tesis},
note = {autor, Xiaochen Zheng,
Advisors, Joaqu\'in Ordieres Mer\'e},
university = {Universidad Polit\'ecnica de Madrid,
Programa DEGIN,
Departamento de Ingenier\'ia de
Organizaci\'on Administraci\'on de Empresas y Estad\'istica,
Universidad Polit\'ecnica de Madrid}
}
  • F. Shrouf, “Utilizing the internet of things to promote energy awareness and efficiency at discrete production processes: practices and methodology,” Tesis PhD Thesis, 2015.
    [Bibtex] 
    @PHDTHESIS{Shrouf2015,
author = {Shrouf, Fadi},
title = {Utilizing the Internet of Things to promote energy awareness and
efficiency at discrete production processes: Practices and methodology},
school = {Industrial Engieering. Universidad Polit\'ecnica de Madrid},
year = {2015},
type = {Tesis},
note = {autor, Fadi Shrouf ; Advisors, Joaqu\'in Ordieres Mer\'e and Giovanni Miragliotta},
owner = {jb},
url = {http://oa.upm.es/37542/},
timestamp = {2015.07.21},
university = {Universidad Polit\'ecnica de Madrid, Departamento de Ingenier\'a de
Organizaci\'on Administraci\'on de Empresas y Estad\'stica,
Universidad Polit\'ecnica de Madrid}
}
  • A. González Marcos, “Desarrollo de técnicas de minería de datos en procesos industriales : modelización en líneas de producción de acero,” PhD Thesis, Logroño, 2006.
    [Bibtex] 
    @PHDTHESIS{GonzalezMarcos2006,
author = {González Marcos, Ana},
title = {Desarrollo de técnicas de minería de datos en procesos industriales
: modelización en líneas de producción de acero},
school = {Industrial Engieering. Universidad de la Rioja},
year = {2006},
address = {Logroño},
note = {una disertación dirigida por Joaquín B. Ordieres Meré y Eliseo P.
Vergara González y desarrollada por Ana González Marcos gráf. ; 30
cm Documento no publicado},
keywords = {Minería de datos Producción industrial Procesos de fabricación Tesis
doctorales F043},
owner = {jb},
pages = {VI, 204 p.},
publisher = {Universidad de La Rioja, Departamento de Ingeniería Mecánica},
timestamp = {2012.05.15}
}
  • A. V. Pernía Espinoza, “Use of advanced numerical simulation techniques for the study and improvement of steel-profiles manufacturing processes : (uso de técnicas avanzadas de simulación numérica para el estudio y mejora de procesos de fabricación de perfiles de acero),” Tesis PhD Thesis, 2007.
    [Bibtex] 
    @PHDTHESIS{PerniaEspinoza2007,
author = {Pernía Espinoza, Alpha Verónica},
title = {Use of advanced numerical simulation techniques for the study and
improvement of steel-profiles manufacturing processes : (uso de técnicas
avanzadas de simulación numérica para el estudio y mejora de procesos
de fabricación de perfiles de acero)},
school = {Industrial Engieering. Universidad de la Rioja},
year = {2007},
type = {Tesis},
note = {author (autor), Alpha Verónica Pernía Espinoza ; Advisor
Joaqu\'in Ordieres Mer\'e.
Departamento de Ingeniería
Mecánica de la Universidad de La Rioja. },
keywords = {Procesos de fabricaci\'on Estructuras tubulares de acero
M\'etodos de simulaci\'on},
owner = {jb},
timestamp = {2012.05.15},
university = {Universidad de La Rioja, Departamento de Ingeniería Mecánica, Universidad
de La Rioja}
}

Related Research Projects

Some research projects are aligned to this research line, such as,

CodeURL of the project / TitleFunding source
DynReacthttp://dynreact.eu/European Commission
ControlInSteelhttps://www.santannapisa.it/it/ricerca/progetti/control-steel-diffusione-e-valorizzazione-dei-risultati-di-rfcs-nel-campo-delleEuropean Commission