Search

The housing sector is important for future energy savings and greenhouse gas emission mitigation. A dynamic, stock-driven and segmented dwelling stock model is applied for dwelling stock energy analyses. Renovation activity is estimated as the need for renovation during the ageing process of the stock, in contrast to exogenously defined and often unrealistic renovation rates applied in other models. The case study of Norway 2016–2050 shows that despite stock growth, the total theoretical estimated delivered energy is expected to decrease from 2016 to 2050 by 23% (baseline) and 52% (most optimistic scenario). A large share of the energy-efficiency potential of the stock is already realized through standard renovation. The potential for further reductions through more advanced and/or more frequent renovation, compared to current practice, is surprisingly limited. However, extensive use of heat pumps and photovoltaics will give large additional future energy savings. Finally, user behaviour is highly important. A strong future rebound effect is expected as the dwelling stock becomes more energy efficient. The estimated total ‘real’ energy demand is expected to decrease by only 1% (baseline) and 36% (most optimistic scenario). Hence, reaching significant future energy and emission reductions in the Norwegian dwelling stock system will be challenging. [ABSTRACT FROM AUTHOR]

A segmented dynamic dwelling stock model is proven useful for understanding the development and changes of ageing building stocks, which is highly relevant for renovation measures and estimates of energy use and emissions in aggregated building stocks. In this paper, such a model is developed further for detailed analyses of dwelling stock energy demand and exemplified for the Norwegian dwelling stock 1960–2015. The dwelling stock model simulates the development in stock size and composition and is combined with archetype-specific energy intensities to estimate the total energy demand. After calibrating the model results with statistics, the model is used to explore the phenomena and causes of historical changes. A large-scale improvement of the energy efficiency of the Norwegian dwelling stock has taken place through renovation and construction of new dwellings. A historical shift to more efficient energy carriers and heating systems has had an effect on energy savings in the system, of the same size as the effect of the improved energy efficiency of the stock. However, the total average energy savings per m 2 are offset by changes in user heating habits. A significant decrease in average delivered energy intensity per m 2 is only observed after the introduction of heat pumps. [ABSTRACT FROM AUTHOR]

Models describing long-term development in energy consumption or greenhouse gas emissions from building stocks commonly model the underlying development in the building stock's size and composition using simple, linear trends. Uncertainty is rarely discussed. This study explores the importance of uncertainty in input parameters in a dynamic model for the Norwegian dwelling stock that is presented in a recent publication and provides valuable insights in expected future quantities of renovation activity and how different segments of the stock are subject to renovation. By use of a sensitivity analysis and a scenario analysis the present study focuses on how uncertainty in model input parameters affect the modeling results and the robustness of results and conclusions. The sensitivity analysis did not lead to unexpected changes in results, and showed the dynamic model being mostly sensitive to changes in population and dwellings’ lifetime. Further scenarios with extreme input values for population and dwellings’ lifetime were considered in order to investigate the consequences of low and high renovation options. Results prove that previous main conclusions still hold: renovation rates at levels necessary to achieve policy targets in energy and emission savings seem unrealistic to be achieved when modeling the ‘natural’ need for renovation. [ABSTRACT FROM AUTHOR]

• A new methodology for energy use and peak load forecasting is developed. • Forecast of total energy use show a decrease between 2 and 11 TWh towards 2050. • Indicators for evaluating the difference between energy forecasts and statistics. • Large differences in how well forecasts predicted energy use towards 2020. • Calibration of energy use models on multiple years may improve energy forecasts. Projections of future energy use in buildings are a crucial tool in the tracking and attainment of political targets for energy efficiency and climate gas mitigation. In this article, a new methodology for projecting both the final energy use and the peak power demand for the Norwegian building stock is presented. The novelty of the methodology is to combine a set of existing, previously documented models in a novel way that integrates building stock models, hourly energy demand load profiles, and energy system modelling. The result is a coherent long-term projection of both annual and hourly energy use for different energy carriers, presented here with four scenarios of final energy use. The results show an expected decrease in total energy use for the Norwegian building stock between −2 and −12 TWh towards 2050, corresponding to a −3% to −14% of the energy use in 2020. Models for projecting future energy use are helpful both to evaluate the potential effects of current policies and to help reveal the need for new or updated policies. However, to have the desired effect, the projections must be as realistic as possible and reflect the actual development in energy use in the building stock. This necessitates a methodology for evaluating historical long-term annual energy use projections to understand why some models succeed in predicting energy use development while others fail. In this article, a set of indicators for evaluating the calibration of different models are presented. The indicators evaluate the initial difference and the divergence in the annualised trend for energy use projection models, compared to statistical data. The indicators are used to compare selected historical energy use projections for the Norwegian building stock against energy use from statistics from 2000 to 2020. The comparison shows large differences between the different projections, where calibrated scenarios show energy savings that tend to be more optimistic in the reference projection but more conservative in the best case potential. [ABSTRACT FROM AUTHOR]