District heating network maintenance planning optimization

Matteo Pozzi; Andrea Bettinelli; Fabrizio Detassis; Ettore Filippini; Simone Graziani; Stefano Morgione; Daniele Vigo

doi:10.1016/j.egyr.2021.08.156

District heating network maintenance planning optimization

Matteo Pozzi^*, Andrea Bettinelli, Fabrizio Detassis, Ettore Filippini, Simone Graziani, Stefano Morgione, Daniele Vigo

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

To ensure the correct functioning of district heating networks and minimize critical failures, utilities allocate every year a significant part of their budget to maintenance operations. In the present work we describe a risk-based approach implemented to tackle the problem of designing optimal multi-year maintenance campaigns, applied to the Italian city of Brescia, showing how data-driven techniques can help decision makers assess the long terms impacts of budget allocations.

Original language	English
Pages (from-to)	184-192
Number of pages	9
Journal	Energy Reports
Volume	7
DOIs	https://doi.org/10.1016/j.egyr.2021.08.156
Publication status	Published - Oct 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s)

Keywords

District heating network
Machine learning
Maintenance planning
Optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.egyr.2021.08.156

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title = "District heating network maintenance planning optimization",

abstract = "To ensure the correct functioning of district heating networks and minimize critical failures, utilities allocate every year a significant part of their budget to maintenance operations. In the present work we describe a risk-based approach implemented to tackle the problem of designing optimal multi-year maintenance campaigns, applied to the Italian city of Brescia, showing how data-driven techniques can help decision makers assess the long terms impacts of budget allocations.",

keywords = "District heating network, Machine learning, Maintenance planning, Optimization",

author = "Matteo Pozzi and Andrea Bettinelli and Fabrizio Detassis and Ettore Filippini and Simone Graziani and Stefano Morgione and Daniele Vigo",

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year = "2021",

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doi = "10.1016/j.egyr.2021.08.156",

language = "English",

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T1 - District heating network maintenance planning optimization

AU - Pozzi, Matteo

AU - Bettinelli, Andrea

AU - Detassis, Fabrizio

AU - Filippini, Ettore

AU - Graziani, Simone

AU - Morgione, Stefano

AU - Vigo, Daniele

PY - 2021/10

Y1 - 2021/10

N2 - To ensure the correct functioning of district heating networks and minimize critical failures, utilities allocate every year a significant part of their budget to maintenance operations. In the present work we describe a risk-based approach implemented to tackle the problem of designing optimal multi-year maintenance campaigns, applied to the Italian city of Brescia, showing how data-driven techniques can help decision makers assess the long terms impacts of budget allocations.

AB - To ensure the correct functioning of district heating networks and minimize critical failures, utilities allocate every year a significant part of their budget to maintenance operations. In the present work we describe a risk-based approach implemented to tackle the problem of designing optimal multi-year maintenance campaigns, applied to the Italian city of Brescia, showing how data-driven techniques can help decision makers assess the long terms impacts of budget allocations.

KW - District heating network

KW - Machine learning

KW - Maintenance planning

KW - Optimization

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SN - 2352-4847

VL - 7

SP - 184

EP - 192

JO - Energy Reports

JF - Energy Reports

ER -

District heating network maintenance planning optimization

Abstract

Bibliographical note

Keywords

UN SDGs

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