As building performance data becomes more pervasive, there are opportunities to improve how we design the net-zero carbon buildings of the future. At Loughborough University, we are utilising real-world data to create a new model that will improve how buildings and, more importantly, communities of buildings, can be designed.

Active buildings can produce, store, and share energy, often in communities. But designing such communities will require new approaches to building energy modelling.

Modelling assumptions

Usually the performance of new dwellings is assessed using monthly quasi-static models, such as SAP. This is useful, as it gives an estimate of annual energy demand (for standardised “typical” occupants) and allows dwellings to be compared on a level playing field. 

However, when we move to active buildings, monthly energy demands do not provide enough temporal detail – we need results at much shorter intervals. This is because active technologies such as storage, demand-side management and renewables vary their output or demand over short timescales.

One way to predict building energy demand over shorter timescales is dynamic thermal modelling, using tools such as Energy Plus. While these are valuable tools, the results are inevitably dependent on the heating times and set-point temperatures being provided. 

The occupants are the key

With both quasi-static and dynamic models, the primary challenge lies with the variability of occupants and their habits. People heat their homes to widely varying temperatures, and there are also important differences between households in the timing of heating. 

This variability becomes increasingly important when we are considering the aggregated demand of a group of buildings. The peaks in demand from different buildings do not necessarily coincide, an effect known as diversity.

As buildings become more efficient, the losses of heat through conduction and cold air ingress are reduced. Therefore, the use of domestic hot water and electricity for lights and appliances become an increasingly large proportion of total energy demand. These energy demands both depend heavily on the habits of the occupants.

Taking a new approach

Rather than the commonly used approaches of quasi-static models or dynamic thermal models, we are taking a quite different approach in developing a new model. We are using monitored data from real buildings, to capture the patterns of energy demand which occur, rather than those assumed for “typical” occupants. This will enable more reliable estimates of energy demand from groups of buildings at an early design stage and more accurate assessments of the impact of active building technologies.  

Our new approach to modelling is only possible because much larger quantities of monitoring data are available now than in the past. This will only increase with the widespread introduction of smart meters and connected devices. Therefore, modelling approaches based on monitored data will become increasingly valuable. While the availability of monitoring data from low-energy buildings is somewhat limited at present, largely because such buildings are still rare, this will improve over time. 

Our aim for the new model is to provide valuable insights into the energy demand of communities of active buildings at a half-hourly level, early design stage. This will provide results closer to the reality of how homes are used by real people and enable more accurate planning and forecasting.

Stephen Watson is a Research Associate at Loughborough University where he is focusing on predicting demands of domestic buildings and developing a new method based on monitored data for the Active Building Centre Research Programme.