True power consumption labeling and mapping of the health system of the Castilla y León region in Spain by clustering techniques

Puente-Gil, Álvaro de la; González-Martínez, Alberto; Borge-Díez, David; Martínez-Cabero, Miguel-Ángel; Simón-Martín, Miguel de

doi:10.1016/j.egypro.2018.11.283

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Based on the dendrogram's shape, different hierarchy methods may be more appropriate. In this case, the Ward's method seems to be the most adequate, as other authors have suggested [38].…”

Section: Clusteringmentioning

confidence: 67%

“…On the other hand, some authors prefer to define clustering as a multi-target optimization problem involving a distance function, a density function and the number of defined clusters. Moreover, the clustering analysis is not an automatic process, but rather, an iterative one (interactive multi-target optimization) which implies a test and fail procedure [37,38].…”

Section: Clusteringmentioning

confidence: 99%

“…The clustering may be hard (each member belongs only to one group) or soft (each member may belong to several groups simultaneously with different belonging rates). Furthermore, there are multiple clustering techniques and algorithms that can be classified in four main categories: The most appropriate algorithm for clustering depends on the problem characteristics and, most often, it must be selected experimentally using the researchers' past experience [5,38].…”

Section: Clusteringmentioning

confidence: 99%

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Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

et al. 2018

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Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators.

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“…Based on the dendrogram's shape, different hierarchy methods may be more appropriate. In this case, the Ward's method seems to be the most adequate, as other authors have suggested [38].…”

Section: Clusteringmentioning

confidence: 67%

Section: Clusteringmentioning

confidence: 99%

Section: Clusteringmentioning

confidence: 99%

See 1 more Smart Citation

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

et al. 2018

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“…Factors leading to such outcomes have been analyzed in detail in recent studies, including mathematical modelling and statistical analysis [27], [28], [31], providing relationships between individual factors and specific annual energy consumption. Opportunities for energy consumption decrease in public health are tightly connected with the end use of energy in this sector.…”

Section: Introductionmentioning

confidence: 99%

Natural Gas Saving And Emissions Decrease In Public Health Care Sector – A Case Study

Mierka

Variny

Skalíková

et al. 2019

Civil and Environmental Engineering Reports

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A study on natural gas saving and emissions decrease in the public health care sector is presented. The analyzed hospital complex belongs to the largest ones in Slovakia and uses both hot water and water steam for heating purposes. Visual steam system inspection revealed serious inefficiencies, including steam venting, missing pipelines insulation and obsolete steam sources with dysfunctional blow-down system. Defined experiment with stepwise steam appliances shutdown enabled quantification of excess natural gas consumption due to these inefficiencies. Measures proposed for the solution of this state are inexpensive, with a short payback period. The expected natural gas savings amount up to 3200 MWh/year, which represents roughly 50% of the total natural gas consumption in the hospital complex.

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“…Equally important is the research on health care buildings. Thus, the authors of paper [15] segmented the buildings of medical institutions by the amount of electricity consumption into clusters that characterize the intensity of resource con sumption and determined the reference energy indices for each building. The segmentation was performed by the Ward method (hierarchical clustering) and the kmeans method (nonhierarchical clustering).…”

mentioning

confidence: 99%

Segmentation of heat energy consumers based on data on daily power consumption

Zagirnyak¹,

Perekrest²,

Ogar³

et al. 2021

Nauk. visn. nat. hirn. univ.

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Purpose. Improving the quality of the analysis of energy consumption modes of buildings of educational institutions by determining the typical patterns of their consumption by the k-means method based on statistics of thermal energy consumed, their area, outdoor and indoor air temperature. Methodology. Methods of statistical and intellectual data analysis, optimization methods, k-means method. Findings. It is shown that most of the researched consumers of thermal energy have similar patterns of its consumption and can be divided into separate segments according to the peculiarities of functioning. The used data set is diverse as it includes three groups of data (conditionally formed by the type of educational institutions). The buildings in groups have different areas, internal temperature to be maintained, operation modes of heating systems and so on. For each of the groups, the number of clusters corresponding to low, medium and high values of heat consumption is determined. It was found that the amount of the consumed heat and consumer behavior depends on the day of the week. The obtained results can be difficult to generalize for all existing types of buildings, but it is expected that communal buildings may have similar consumption patterns, as operating modes and norms of internal microclimate remain the same for different temperature zones and territories. Originality. The method for dividing the consumers of thermal energy into segments on the basis of the data on daily energy consumption applying the k-means clustering algorithm, implemented in the programming language R, is further developed. For the first time, it has been proposed to consider not one, but three groups of objects of the educational institutions. It is proposed to determine the modes of energy consumption by buildings analytically by taking into account such parameters as the area of buildings, the amount of heat consumed by them, external and internal air temperature. Practical value. The results of the research are useful for heat supply companies, municipalities and can be used to develop programs and policies for energy efficiency. The given information and analytical support create a basis for the development of software solutions with the function of integration into local energy monitoring systems of a separate building, energy management system of the district and/or city. The presented results provide an opportunity to predict the cost of energy resources used for heating buildings, which increases the efficiency of engineering systems of buildings.

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True power consumption labeling and mapping of the health system of the Castilla y León region in Spain by clustering techniques

Cited by 7 publications

References 11 publications

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

Natural Gas Saving And Emissions Decrease In Public Health Care Sector – A Case Study

Segmentation of heat energy consumers based on data on daily power consumption

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