Electrical consumption forecasting in hospital facilities: An application case

Bagnasco, Andrea; Fresi, F.; Saviozzi, M.; Silvestro, F.; Vinci, Andrea

doi:10.1016/j.enbuild.2015.05.056

Cited by 100 publications

(50 citation statements)

References 25 publications

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“…As previously reported in literature [5] [6] [7], it is possible to see how energy consumption modulates very little between night and day, which is typical for an health care facility offering different and highly specialized services such as the San Martino hospital, including an extensive emergency room working 24/7 every day of the year It is also possible to detect that the most energy consuming facilities are:…”

Section: Application Of the Energy Monitoring Systemmentioning

confidence: 50%

Energy efficient policy and real time energy monitoring in a large hospital facility: A case study

Silvestro¹,

Bagnasco²,

Lanza³

et al. 2017

IJHT

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The San Martino University Hospital is the largest medical facility in the city of Genova. Internal electric power distribution takes place via a 15 kV Medium Voltage grid, connected to the ENEL distribution network via two Points of Delivery (PoD). Two co-generators were installed for a total of 3 MW generation capacity. Loads add up to a total of 4 MW. The network has a ring configuration and it is composed of 14 MV/LV cabins working in an in-out configuration. The San Martino hospital has implemented a real time energy monitoring system. The system is used as a tool to supervise energy demand at every MV/LV cabin and to support the management policies of buildings and technical facilities, in order to optimize energy consumption by pursuing energy saving opportunities. This paper presents how the real time monitoring system was implemented interfacing directly the MV protections. It presents the process of assessing the consumption profiles of the hospital facilities, in order to highlight issues and identify the actions to reduce energy waste. A specific case study is developed and conclusions drawn as to how a monitoring system can supply its users with a tool to improve efficiency and reduce costs.

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Section: Application Of the Energy Monitoring Systemmentioning

confidence: 50%

Energy efficient policy and real time energy monitoring in a large hospital facility: A case study

Silvestro¹,

Bagnasco²,

Lanza³

et al. 2017

IJHT

View full text Add to dashboard Cite

show abstract

“…Energy consumption forecasting provides valuable information for: a) evaluating consumption profiles, b) detecting energy saving opportunities, and c) designing energy efficiency measures in existing buildings [3]. Knowledge of building energy demand profiles is an integral step in the design of energy saving initiatives.…”

Section: Introductionmentioning

confidence: 99%

“…Large Medical Centers have specific features which clearly distinguish them from other kinds of buildings and difficult the energy modeling tasks: 1) a continuous energy and electricity utilization is required; 2) difficulties in measuring detailed building occupancy; 3) complex and energy-intensive facilities (surgeries rooms, TAC machines) are usually subject to unpredictable events; and 4) restrictive requirements of air quality and indoor comfort temperature. Besides, any energy saving initiatives must preserve healthcare delivery [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

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Energy consumption modeling by machine learning from daily activity metering in a hospital

Ruiz

Pacheco-Torres

Casillas

2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Hospitals are large buildings that consume a great amount of energy mostly due to their continuous energy consumption needs, high consumer medical equipment, and special requirements of thermal and air conditions. Reliable dynamic simulation is a chimera because of the complex design and behavior of these buildings. Therefore, monitoring-based methods arise as a plausible alternative. Its main drawback, however, is the lack of enough data to generate statistically robust models.The paper faces this problem thanks to the helpful contribution of a collaborative hospital which was able to generate daily data of electrical energy consumption for a period of six years. Besides, thirteen variables that summarize the daily activity of the hospital are also included. The results show how machine learning techniques generate models that accurately predict the electrical energy consumption based on weather conditions and activity measurements.The obtained results are useful for the design of more specific energy saving strategies, a more efficient economic investment for energy retrofitting of existing buildings and a better management of economic energy cost in large-scale buildings.

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“…Christiansen et al [16] presented a methodological approach, for a more accurate assessment of the electrical energy demand of medical equipment operated within modern hospital laboratories, to verify that the medical equipment is responsible for high electricity consumption in hospital laboratories, and found that only a few plug load groups contributed to the greater part of the total electrical energy demand. Bagnasco et al [17] developed a load forecasting model to forecast electrical consumption a dayahead in the Cellini medical clinic of Turin, Italy. They concluded that the electrical load forecasting and modeling is an essential component for better dynamic management of the thermal behavior of buildings for energy saving policy.…”

Section: Introductionmentioning

confidence: 99%

Power Consumption Analysis of Electrical Installations at Healthcare Facility

et al. 2017

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This paper presents a methodology for power consumption estimation considering harmonic and interharmonic content and then it is compared to the power consumption estimation commonly done by commercial equipment based on the fundamental frequency, and how they can underestimate the power consumption considering power quality disturbances (PQD). For this purpose, data of electrical activity at the electrical distribution boards in a healthcare facility is acquired for a long time period with proprietary equipment. An analysis in the acquired current and voltage signals is done, in order to compare the power consumption centered in the fundamental frequency with the generalized definition of power consumption. The results obtained from the comparison in the power consumption estimation show differences between 4% and 10% of underestimated power consumption. Thus, it is demonstrated that the presence of harmonic and interharmonic content provokes a significant underestimation of power consumption using only the power consumption centered at the fundamental frequency.

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Electrical consumption forecasting in hospital facilities: An application case

Cited by 100 publications

References 25 publications

Energy efficient policy and real time energy monitoring in a large hospital facility: A case study

Energy efficient policy and real time energy monitoring in a large hospital facility: A case study

Energy consumption modeling by machine learning from daily activity metering in a hospital

Power Consumption Analysis of Electrical Installations at Healthcare Facility

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