Abstract
This work uses the outcome of a computational tool that performs Energy Performance Certification (EPC) data processing and transforms raw data into comparable data. Multi-correlation among variables results in probability distributions for the most relevant form and fabric building parameters. The model consistently predicts the distributions for heating and cooling energy needs for the Lisbon Metropolitan Area, with an error below 7% for the first, second and third quartiles. Differences in the energy needs estimation are below 6% when comparing the seasonal steady-state with the resistance-capacitance (RC) model, which proved to be a robust alternative algorithm capable of modeling hourly user profiles. The RC model calculates electricity consumption for actual, adequate, and minimum thermal comfort scenarios corresponding to different user profiles. The actual scenario, built from statistics and a previous survey, defines a reference to evaluate other scenarios for the mean electricity consumption for space heating and cooling in the building units with those systems. The results show that the actual mean electricity consumption for heating (610 kWh/y) is slightly above the minimum (512 kWh/y), with 37% of building units potentially under heated. The electricity consumption (108 kWh/y) for cooling is below the minimum (129 kWh/y).
Highlights
Predicting the heating and cooling energy consumption of the building stock is critical to delineate the required renovation strategies according to the Renovation Wave for Europe program [1]
Validate the building stock model validation (Section 3.1); Compare hourly and seasonal methods (Section 3.2); Compare actual electricity consumption with average data from statistics (Section 3.3.2); Compare calculated electricity consumption for adequate and minimum conditions with the calculated for actual conditions (Section 3.3.3)
The results show that the computed electricity for space heating in actual conditions (B) represents 17% of the overall building stock electricity consumption (Figure 6), which is above the accounted consumption in 2010 and 2020 national surveys, 9.1% [40] and
Summary
Predicting the heating and cooling energy consumption of the building stock is critical to delineate the required renovation strategies according to the Renovation Wave for Europe program [1]. The same applies to understanding the paradox of excess mortality in mild winter climates and its relation to energy poverty [2,3] or accounting for the building sector’s share for regional and national energy and climate plans (NECP) [4]. It is not less important to understand how buildings perform to more extensive heat waves, and the corresponding impact on summer mortality [5]. A recent review [8] cited almost 300 references, describing numerous models and techniques with the common goal of predicting building stock energy consumption, covering a spatial scale from a city block to an entire city
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