Analysing the suitability of buildings for energy saving services is a costly process. The Sim4Blocks project has been examining the effectiveness of two approaches for assessing the demand response capabilities of buildings, with the aim of increasing the implementation of flexible energy services across the EU on a large scale in a quick and cost-effective way.
The energy sector faces many challenges, including unbalanced grids, peak load problems and power failures. For demand response services to overcome some of these, personal household interests need to be addressed. Utility companies and aggregators need initiatives that incentivise demand response services for residents that ultimately benefit the grid.
However, measuring the potential energy flexibility of one building is time consuming and costly. The lack of widespread available data also makes it hard for utility services to demonstrate and explain to households the benefits and demand response services suitable for their building type.
Sim4Blocks, an EU-funded project, has been testing models to make it easier to estimate the demand response capabilities of large residential EU building stock so they can contribute to flexibility and grid stability. Working closely with researchers from HFT Stuttgart and University College Dublin’s Electricity Research Centre, two techniques have been tested: classification and clustering.
Classification uses domain knowledge and expertise to determine which class each building belongs to from a set of predefined classes. 3D building models can be generated using the technical data available for the building, providing data on the age, use, number of floors, type, refurbishment year, window surface and insulation.
Clustering uses algorithms to filter data points into groups to identify whether there are any similarities between them. A cluster might represent buildings with similar attributes in demographic, economic, geometric and annual heating demand. This technique not been widely used before for the thermal determination of buildings.
The data these two techniques provide will make it easier to group buildings based on certain characteristics and datasets and how this affects their ability to deliver flexible energy. Utilities will be able to use this data to deliver the most appropriate demand response services to specific blocks of buildings, districts and municipalities.
Both techniques can help aggregators, cluster managers and retail electricity companies see when there is high and low energy intensity, seasonal functions, time of day patterns and help set more personal electricity tariffs for customers.
An explanatory factsheet has been produced by Sim4Blocks to explain in more detail how each technique works and the data it can gather. Read it here.