2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

An introduction to heat transfer

Computational Fluid Dynamics for urban physics: Importance, scales, possibilities, limitations and ten tips and tricks towards accurate and reliable simulations

Applications of Artificial Intelligence and Machine learning in smart cities

Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management

Heating and cooling building energy demand evaluation; A simplified model and a modified degree days approach

Managing supply and demand in the electricity grid is becoming more challenging due to the increasing penetration of variable renewable energy sources. As significant end-use consumers, and through better grid integration, buildings are expected to play an expanding role in the future smart grid. Predictive control allows buildings to better harness available energy flexibility from the building passive thermal mass. However, due to the heterogeneous nature of the building stock, developing computationally tractable control-oriented models, which adequately represent the complex and nonlinear thermal-dynamics of individual buildings, is proving to be a major hurdle. Data-driven predictive control, coupled with the “Internet of Things”, holds the promise for a scalable and transferrable approach, with data-driven models replacing traditional physics-based models. This review examines recent work utilising data-driven predictive control for demand side management application with a special focus on the nexus of model development and control integration, which to date, previous reviews have not addressed. Further topics examined include the practical requirements for harnessing passive thermal mass and the issue of feature selection. Current research gaps are outlined and future research pathways are suggested to identify the most promising data-driven predictive control techniques for grid integration of buildings.

Data-driven predictive control for unlocking building energy flexibility: A review

Demand response algorithms for smart-grid ready residential buildings using machine learning models

The detailed analysis of a solar collector is a complex task, due to the high number of parameters affecting its performance. In the last 40 years, several dynamic procedures have been developed and tested using numerical approaches, to obtain the behavior of the thermal solar collector without performing the set of complicated and expensive experimental tests usually adopted in steady-state approaches. Moreover, thanks to the improvement of the computing performance, these numerical models provide useful tools in reproducing for complex system behavior. In fact, when multiple energy sources are coupled together to build integrated systems (i.e., Solar-Assisted Heat Pumps, Ground-Source Solar-Assisted Heat Pump, etc.) the dynamics of each equipment has an noticeable influence on the behavior of the whole system. Therefore, these tools can be also profitably used to develop and optimize dynamic control criteria for these systems. In this context, a great effort has been made in the last years to improve the predictive potential of the dynamic models for solar collectors. Finally, thanks to the increase of the computational performance in the last years, Computational Fluid-Dynamics (CFD) approach has become a powerful tool to investigate the heat transfer phenomena. A lot of works have been made using both commercial and in-house developed codes, investigating several aspects concerning the heat transfer mechanism in a solar collector. In the present work, an updated review of models for flat-plate thermal solar collectors is presented, including a proper classification and a description of their main characteristics and performance. A short description of the main works involving CFD analysis on thermal solar collectors is reported too.

Mattia De Rosa

Papers

Heating and cooling building energy demand evaluation; A simplified model and a modified degree days approach

Data-driven predictive control for unlocking building energy flexibility: A review

Demand response algorithms for smart-grid ready residential buildings using machine learning models

Dynamic thermal models and CFD analysis for flat-plate thermal solar collectors - A review

Historical trends and current state of heating and cooling degree days in Italy