Harvesting big data from residential building energy performance certificates: retrofitting and climate change mitigation insights at a regional scale

Abstract

The reduction of energy consumption and the increase in energy efficiency is currently an important cornerstone of EU policy. Energy performance certificates (EPCs) were implemented as one of the tools to promote this agenda, and are used for the energy performance assessment of buildings. In this study, the characteristics of the Portuguese dwelling stock are regionally analysed using data from approximately 523,000 Portuguese residential EPCs. Furthermore, a bottom-up building typology approach is used to assess the regional energy needs impact of retrofitting actions and to estimate the heating and cooling energy performance gaps of the whole dwelling stock, as well as the potential CO 2 emissions resulting from the gaps' potential offset due to increase thermal comfort. The results show that Portuguese residential buildings have very low energy performance, with windows and roofs being identified as the most energy inefficient elements. Roof retrofitting has the highest potential for the reduction of energy needs. The estimated heating and cooling energy performance gap amount to very significant percentages, due to the poor performing building stock but also very low energy consumption levels, with probable consequences for the thermal comfort of occupants. Assuming the current energy mix, carbon emissions would be 9.8 and 20.2 times higher associated with heating and cooling, respectively, if the actual final energy consumption were to match the estimated theoretical values derived from building regulation. This study demonstrates several application cases and leverages the potential of the individual EPC, increasing the detail in the dwelling stock characterization and energy performance estimation, revealing its value for energy retrofit and climate change mitigation assessments, as well as establishing the ground for future work related to building retrofits, energy efficiency measure implementation, climate change mitigation, thermal comfort, and energy poverty studies.

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

“…Other studies [6,14,49] report that occupants turned off the installed MVHR (mechanical ventilation with heat recovery) and used windows to ventilate instead, with significant energy implications. Similar discrepancies were found with the operation of shading devices [6,22,23,88,90] in approximately 10% of the above studies, leading to higher or lower solar gains than modelled.…”

“…Statistical data on building stock were also a source of data for large-scale projects [62,75,80,88]. Most of the publications (76%) used aggregated annual data on energy use, while 10% of studies used monthly data.…”

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

“…And if we increase the resolution of the data (Jimenez et al, 2019), we can also understand the causal relationships related to consumption. In terms of infrastructure load, patterns of population movement (Gurram et al, 2019) offer exciting opportunities, but can also be integrated with the condition of buildings (Gouveia and Palma, 2019), which also supports the satisfaction of urban planning tasks (Milojevic-Dupont et al, 2020) at a higher level.…”

“…By extracting time series data (Joshi et al, 2019) from satellite imagery, we can indirectly validate the models by comparing the time series or identify the factors of potato disease (Fenu and Malloci, 2019). In urban developments (Milojevic-Dupont et al, 2020) and in building condition surveys (Gouveia and Palma, 2019) the forecast shows the development of infrastructure expansion and maintenance, to which the probability of flood protection problems (Avand et al, 2021) can also be linked.…”

“…And comparable to other approaches (Jimenez et al, 2019), which strengthens confidence in the models (Qin and Chi, 2020). Better resolution data supports marine habitat protection planning (Coro et al, 2020), risk assessment input (Fenu and Malloci, 2019), but can also be used (Gouveia and Palma, 2019) to analyze building consumption data. The efficiency of downscaling techniques can be increased with the Internet of Things (Lambrinos, 2019) toolbar.…”