https://hdl.handle.net/10807/214 2026-06-27T04:58:08Z 2026-06-27T04:58:08Z https://hdl.handle.net/10807/341111 2026-06-27T00:14:06Z 2026-01-01T00:00:00Z

Titolo: Designing For Tensions: A Nascent Design Theory For Decision Support Systems In Renewable Energy Communities Autori: Michele Cipriano; Francesco Virili Abstract: Renewable Energy Communities (RECs) are characterised by the simultaneous pursuit of economic efficiency and social inclusion. However, existing decision support systems (DSSs) are largely grounded in optimisation logics that reduce such multidimensional objectives to static trade-offs, overlooking their persistent and interdependent nature. This research argues that RECs expose a limitation in current DSS design, considering the lack of mechanisms to represent and manage enduring tensions between competing goals. Adopting a Design Science Research (DSR) approach, we propose an emerging design perspective centred on the notion of tension-aware DSS. We articulate a nascent design theory that integrates insights from paradox theory with data warehouse design principles, enabling the representation and exploration of socio-economic tensions across time and stakeholder levels. The contribution lies in reframing DSSs from tools for optimisation to infrastructures supporting the navigation of persistent tensions in sustainability contexts.

2026-01-01T00:00:00Z https://hdl.handle.net/10807/340384 2026-06-26T00:10:57Z 2017-01-01T00:00:00Z

Titolo: Forecasting natural gas flows in large networks Autori: Mauro Dell’Amico; Natalia Selini Hadjidimitriou; Thorsten Koch; Milena Petkovic Abstract: Natural gas is the cleanest fossil fuel since it emits the lowest amount of other remains after being burned. Over the years, natural gas usage has increased significantly. Accurate forecasting is crucial for maintaining gas supplies, transportation and network stability. This paper presents two methodologies to identify the optimal configuration o parameters of a Neural Network (NN) to forecast the next 24 h of gas flow for each node of a large gas network. In particular the first one applies a Design Of Experiments (DOE) to obtain a quick initial solution. An orthogonal design, consisting of 18 experiments selected among a total of 4.374 combinations of seven parameters (training algorithm, transfer function, regularization, learning rate, lags, and epochs), is used. The best result is selected as initial solution of an extended experiment for which the Simulated Annealing is run to find the optimal design among 89.100 possible combinations of parameters. The second technique is based on the application of Genetic Algorithm for the selection of the optimal parameters of a recurrent neural network for time series forecast. GA was applied with binary representation of potential solutions, where subsets of bits in the bit string represent different values for several parameters of the recurrent neural network. We tested these methods on three municipal nodes, using one year and half of hourly gas flow to train the network and 60 days for testing. Our results clearly show that the presented methodologies bring promising results in terms of optimal configuration of parameters and forecast error.

2017-01-01T00:00:00Z https://hdl.handle.net/10807/340371 2026-06-26T00:11:13Z 2021-01-01T00:00:00Z

Titolo: Enhancing port’s competitiveness thanks to 5G enabled applications and services Autori: Andrea Porelli; Natalia Selini Hadjidimitriou; Mariangela Rosano; Stefano Musso Abstract: This work aims to evaluate a set of Critical Success Factors (CSF) that are important for port operations optimization. Furthermore, a set of 5G enabled applications is evaluated based on their importance for two typologies of companies located in the port of Hamburg, Athens and Luka Koper. More specifically, the importance of CSFs and 5G enabled applications and services is assessed based on the point of views of respondents working for technological companies and companies involved in the port’s operations, using Multi Criteria Analysis. Finally, the relationship between the CSFs and 5G applications and services is considered based on the χ2 test of hypothesis. Then, the possibility to promote 5G applications and services as CSF for port operations optimization which will in turn increase port competitiveness, is discussed.

2021-01-01T00:00:00Z https://hdl.handle.net/10807/340340 2026-06-26T00:11:12Z 2020-01-01T00:00:00Z

Titolo: Assessing the Impact of Shared L-Category Electric Vehicles in six European cities Autori: Mauro Dell’Amico; Natalia Selini Hadjidimitriou; Giulia Renzi

2020-01-01T00:00:00Z