Andrea Menapace

A Methodology for Developing and Benchmarking Burst Detection Tools in Water Distribution Systems

Agnese Travaglia, Devid Tarolli, Ariele Zanfei, Andrea Menapace

Sustainable and reliable operation of water distribution systems requires timely detection of bursts and effective control of real water losses. The digitalisation of water utilities and the increasing deployment of smart monitoring infrastructures are enabling continuous monitoring and diagnostic support, but the …

it (code pays fourni par la source)

Unlocking Waste Heat Potential for District Heating Systems: An Hourly Mapping Methodology

Andrea Menapace, Daniele Anania, Giovanni Dalle Nogare, Rosanna Paradiso et autres

The transition towards climate-neutral energy systems requires exploiting local renewable and residual energy sources to decarbonise the heating and cooling sector. District Heating and Cooling Networks (DHCNs) are key infrastructures for integrating Waste Heat (WH) into urban energy systems, but comprehensive spatial …

it, dk, at (code pays fourni par la source)

Assessing supervised machine learning practice in urban water networks: A critical review of methodological transparency, reproducibility and reporting

Martin Oberascher, Bruno Brentan, Andrea Menapace, Manuel Herrera et autres

In recent years, the advantages of machine learning (ML) have been clearly demonstrated in research on urban water infrastructure (UWI) and has been applied in a wide range of applications. This review critically assesses the current quality of ML implementations in UWI …

at, br, it, gb, nl (code pays fourni par la source)

Ospitaletto District Heating Expansion – Building Heat Demand and Network Dataset

Andrea Menapace, Rosanna Paradiso

This dataset provides georeferenced building-level annual heating demand estimates and district heating network geometry for the municipality of Ospitaletto (Brescia, Lombardy, Italy), developed to assess expansion opportunities for an existing 5th Generation District Heating (5GDHC) network. The building heat demand layer (Ospitaletto_heat.gpkg) …

it (code pays fourni par la source)

Ospitaletto District Heating Expansion – Building Heat Demand and Network Dataset

Andrea Menapace, Rosanna Paradiso

This dataset provides georeferenced building-level annual heating demand estimates and district heating network geometry for the municipality of Ospitaletto (Brescia, Lombardy, Italy), developed to assess expansion opportunities for an existing 5th Generation District Heating (5GDHC) network. The building heat demand layer (Ospitaletto_heat.gpkg) …

it (code pays fourni par la source)

Detection and Classification of Anomalies in Water Distribution Systems

Maria Stergiadi, Farshid Mahmoudabadi, Andrea Menapace, Anton Dignös et autres

it (code pays fourni par la source)

Deep learning surrogate model to explain decision variable synergies in energy system modelling

Matteo Giacomo Prina, Carlo Pelizzoni, Mackenzie Judson, Madeleine McPherson et autres

Energy system models are powerful tools for planning the low-carbon transition. They identify optimal technology portfolios but rarely explain why certain combinations outperform others. Understanding decision variable synergies across multiple sectors remains a key gap in the literature. This study addresses that …

it, ca (code pays fourni par la source)

Topology-aware neural networks for abnormal consumption detection and location in water distribuition networks

João Caetano, Nelson Carriço, Bruno Brentan, Andrea Menapace et autres

This paper presents a topology-aware neural network approach for the detection, location, and quantification of abnormal consumptions in water distribution networks. The approach includes two main steps: the optimization of pressure sensor locations to maximize measurement sensitivity and the development of metamodels …

pt, br, it (code pays fourni par la source)

Topology‐Aware Neural Networks for Abnormal Consumption Detection and Location in Water Distribution Networks

J. Machado Caetano, Nelson Carriço, Bruno Brentan, Andrea Menapace et autres

Abstract This paper presents a topology‐aware neural network approach for the detection, location, and quantification of abnormal consumptions in water distribution networks. The approach includes two main steps: the optimization of pressure sensor locations to maximize measurement sensitivity and the development of …

pt, br, it (code pays fourni par la source)

An Explainable Deep Learning Framework for Zonal Electricity Demand Forecasting: Application to Italy’s Bidding Zones

Benjamin Kwaku Nimako, Andrea Menapace, Matteo Giacomo Prina, Bruno Brentan

it, br (code pays fourni par la source)

Topology-Aware Neural Networks for Abnormal Consumption Detection and Location in Water Distribution Networks

João Caetano, Nelson Carriço, Bruno Brentan, Andrea Menapace et autres

This repository contains the official implementation of topology-aware neural networks for the detection, location, and quantification of abnormal consumptions in water distribution networks (WDNs). The methodology combines genetic algorithm-based optimization for pressure sensor placement with Graph Neural Network (GNN) metamodels that leverage …

Topology-Aware Neural Networks for Abnormal Consumption Detection and Location in Water Distribution Networks

João Caetano, Nelson Carriço, Bruno Brentan, Andrea Menapace et autres

This repository contains the official implementation of topology-aware neural networks for the detection, location, and quantification of abnormal consumptions in water distribution networks (WDNs). The methodology combines genetic algorithm-based optimization for pressure sensor placement with Graph Neural Network (GNN) metamodels that leverage …