Energy Saving Predictions in the Residential Building Sector # An Assessment based on Stochastic Modeling

Abstract

Energy Saving Predictions in the Residential Building Sector - An Assessment based on Stochastic Modeling Energy savings in the residential building sector are typically predicted by means of simplified, normative calculation tools, relying on standardized user behaviour. In reality however, actual energy savings prove to be only 20 to 60% of those predicted, seriously questioning the use of these tools in reliable cost efficiency analyses and robust policy making. Additionally, the tools are mostly conceived deterministically, giving no insight in the uncertainties inherent to predicting energy savings. The main aim of this work is to provide a more reliable energy saving prediction method, embedded in a probabilistic framework. To do so, an evidence-based probabilistic behavioural model is developed, reflecting the large variety in dwelling use. Key aspects of the final behavioural model are (i) the use of time-dependent occupancy profiles and (ii) the implementation of space-dependent heating patterns. As the simple thermal building models of the normative tools are no longer suitable to implement this behavioural model, a transient zonal building model is set up as well. By using the well-known Monte-Carlo technique, energy saving predictions can be generated in terms of probability distributions. When applied on an existing case study district, the results show the above methodology is able to predict energy use estimates that are very comparable to measured data (both in average values and statistical spread), confirming its overall reliability. In addition, and in contrast to the simplified calculation tools, the methodology is capable of capturing typical retrofitting effects like the temperature takeback. Finally, the probabilistic setup proves to be worthwhile in assessing energy savings at a large-scale building stock level (district, city, region, ...): as the building parameters can be conceived probabilistic as well, it allows for an incorporation of the global uncertainty of statistical building stock data within the final energy saving estimates.nrpages: 204status: publishe