A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Increasing Reliability of Bottom-Up Building-Stock Energy Models Using Available Data-Driven Techniques
With the most recent, unprecedented energy crisis ongoing, in which the strain on energy supply and reserves has skyrocketed the energy pricing and the demand for renewable energy systems, popular attention has been drawn once more to energy use in buildings. Since the first energy crisis in the early 70s and later the awareness of climate change, the building energy sector has been revolving around energy conservation and energy efficiency such that they are no longer just buzzwords. Nevertheless, the road towards net zero energy in buildings is far from completed and further strains are needed. To do so, simulation models have become increasingly prominent tools to aid decision-making processes and building energy policy making as they allow for the quick evaluation of competing policy options concerning the best energy conservation recommendations in the building sector. However, a large number of large-scale statistical studies in different countries on the gap between the real and regulatory calculated building energy use, from the last decade, revealed that the regulatory calculation methods (i.e. simplified (white-box) Building-Stock Energy Models) largely overestimated the real energy use of existing, old residential buildings (thus dwellings where energy conservation measures are most needed), inflated true energy savings and undermined national energy policy making. The regulatory calculation methods thus prove to be inaccurate predictors of the real energy use in residential buildings. The prediction errors (i.e. the gap between the real and the theoretical energy use in buildings) vary largely from one house and household to the other and above all, the predictions are not accurate on average either. Also the predicted energy savings are rarely achieved. This PhD-dissertation contributes to research on the gap between the real and theoretical energy use in buildings, focusing on methods to increase the reliability of bottom-up Building-Stock Energy Models using available data-driven techniques. Using ...
Increasing Reliability of Bottom-Up Building-Stock Energy Models Using Available Data-Driven Techniques
With the most recent, unprecedented energy crisis ongoing, in which the strain on energy supply and reserves has skyrocketed the energy pricing and the demand for renewable energy systems, popular attention has been drawn once more to energy use in buildings. Since the first energy crisis in the early 70s and later the awareness of climate change, the building energy sector has been revolving around energy conservation and energy efficiency such that they are no longer just buzzwords. Nevertheless, the road towards net zero energy in buildings is far from completed and further strains are needed. To do so, simulation models have become increasingly prominent tools to aid decision-making processes and building energy policy making as they allow for the quick evaluation of competing policy options concerning the best energy conservation recommendations in the building sector. However, a large number of large-scale statistical studies in different countries on the gap between the real and regulatory calculated building energy use, from the last decade, revealed that the regulatory calculation methods (i.e. simplified (white-box) Building-Stock Energy Models) largely overestimated the real energy use of existing, old residential buildings (thus dwellings where energy conservation measures are most needed), inflated true energy savings and undermined national energy policy making. The regulatory calculation methods thus prove to be inaccurate predictors of the real energy use in residential buildings. The prediction errors (i.e. the gap between the real and the theoretical energy use in buildings) vary largely from one house and household to the other and above all, the predictions are not accurate on average either. Also the predicted energy savings are rarely achieved. This PhD-dissertation contributes to research on the gap between the real and theoretical energy use in buildings, focusing on methods to increase the reliability of bottom-up Building-Stock Energy Models using available data-driven techniques. Using ...
Increasing Reliability of Bottom-Up Building-Stock Energy Models Using Available Data-Driven Techniques
Van Hove, Matthias (author) / Laverge, Jelle / Delghust, Marc
2023-01-01
Theses
Electronic Resource
English
DDC:
690
A review of bottom-up building stock models for energy consumption in the residential sector
| British Library Online Contents | 2010
A review of bottom-up building stock models for energy consumption in the residential sector
| Online Contents | 2010