CurveBlock
Energy performance for a proposed energy positive building is modelled by combining building physics simulations with system performance forecasts and site level inputs to estimate annual energy flows and surplus generation; investors should focus on assumptions for occupancy patterns, weather data, system degradation and maintenance costs plus verification and sensitivity testing.
What does modelling an energy positive building mean in practice?
Modelling an energy positive building is the process of predicting how much energy the building will use and produce over time. Modellers combine architectural geometry with fabric performance data and mechanical and electrical system specifications to simulate hourly energy demand and on site generation such as solar photovoltaics together with storage systems. The goal is to demonstrate that, on a net annual basis, the building produces more energy than it consumes when measured on agreed system boundaries.
Modelling translates design details into anticipated hourly energy flows for a full year of operation.
Key components of a typical modelling workflow
There are four main components that most models bring together.
Building fabric and geometry
Models use the building form and construction details to estimate heat losses and gains. U values for walls roof and windows are combined with ventilation rates to derive heating and cooling loads under standard occupancy and internal gains assumptions.
Occupancy and operation schedules
Predicted occupant numbers and schedules affect internal heat gains lighting use and equipment loads. Assumed thermostat set points hot water usage and ventilation strategies also shape energy demand profiles.
On site generation and storage
Solar PV arrays are modelled with panel orientation tilt and shading factors plus local irradiance data. Battery systems are modelled for charge and discharge behaviour and round trip efficiency. Together these produce an hourly generation and storage profile that is energy balanced against demand.
Plant and controls
Heating cooling ventilation and hot water plant are modelled with declared seasonal efficiency data and control strategies. Where heat recovery or smart controls are proposed their expected performance is represented in the model inputs.
Accurate geometry and system definitions underpin reliable simulated energy demand and supply profiles.
Data inputs and standardised methods
Energy modelling relies on established software and standards to ensure repeatable results. Common tools use dynamic simulation engines to run hourly calculations for a representative year. Weather files are typically drawn from recognised databases and represent typical meteorological years. The choice of standardised method determines how gains losses and efficiencies are calculated and reported.
The credibility of a model depends on the provenance of weather and material performance data and the simulation method used.
Common assumptions that materially affect predicted performance
Models require many assumptions and some of the most influential are often those that investors should scrutinise carefully.
Weather and climate assumptions
Most models use a typical year weather file which averages out extremes. This choice affects solar generation and heating demand estimates and does not capture future climate change impacts unless specifically adjusted.
Occupancy behaviour
Assumed presence patterns thermostat set points appliance use and plug loads have large effects on predictions. Models often use standardised schedules that can understate variability between households or commercial tenants.
Equipment efficiency and degradation
Initial efficiency values for PV modules batteries and heat pumps are typically based on manufacturer data. Assumptions about annual degradation rates for panels and capacity fade for batteries influence long term surplus estimates.
System availability and maintenance
Assumptions about maintenance regimes downtime and inverter or control faults affect expected annual generation. Conservative models include allowances for availability loss and performance drift over time.
Measurement boundaries
Models must state whether energy positive is measured at site boundary meter or at point of use after losses. The boundary choice changes reported surplus values and the nature of any export credits.
Small variations in behaviour and degradation assumptions can change annual surplus estimates by a large margin.
How uncertainty is handled through sensitivity and scenario testing
Because assumptions are uncertain good practice is to run sensitivity analyses and alternative scenarios. Typical analyses vary occupancy patterns weather years and component degradation rates to show a range of possible outcomes. Scenario testing can include lower than expected solar yield or higher than expected internal gains to reveal downside exposures.
Report outputs that present probabilistic ranges rather than single point estimates give a clearer view of risk and likelihood. Models that include stress cases for extreme weather or prolonged equipment outage are more informative for investors who want to understand downside potential.
Scenario and sensitivity testing turn single point forecasts into a range of realistic outcomes for better decision making.
Verification and post occupancy evaluation
Projected performance should be verifiable after occupation. The best practice route sets out a plan for measuring actual energy flows and for reconciling predicted and measured results. Independent checking of model inputs and assumptions by an accredited assessor increases credibility. Planned post occupancy monitoring helps identify if assumed behaviours or system performance diverge from modelled expectations so corrective action can be taken.
Clear measurement and verification plans are essential to close the gap between predicted and actual energy performance.
What investors should look for in an energy model
Investors assessing a proposed energy positive building should look for transparent input data clear statement of measurement boundaries and evidence of sensitivity testing. Check whether weather files are standard or adjusted and whether occupancy patterns are conservative or optimistic. Look for declared degradation rates for PV and batteries and for allowances for maintenance and downtime. Confirm that the model outlines how surplus energy is treated and whether any certificates or export arrangements have been accounted for.
It is also relevant to know how model assumptions are validated and if there is a plan for post occupancy verification. CurveBlock is approved for Gate 1 of the Bank of England and FCA Digital Securities Sandbox and operates a regulated platform offering digital shares so disclosure and verification plans are important parts of what we present to prospective investors. Sandbox approval is not full FCA authorisation and investors should note that difference.
For more on how system level claims are measured and verified see How environmental claims for energy positive buildings are measured and verified and to understand the platform technology behind our share offering see What is Hyperledger Besu and why CurveBlock uses it for digital shares.
Investors should prioritise transparent inputs sensitivity analysis and a clear verification plan when reviewing energy models.
Frequently asked questions
How accurate are energy models for new buildings?
Accuracy varies with input quality and model detail; good models with conservative assumptions and verification plans tend to be within a modest range of measured performance once operational practices are accounted for.
What is a typical degradation assumption for solar panels and batteries?
Models commonly assume 0 to 1 per cent per year for panels and 2 to 5 per cent capacity fade for batteries depending on chemistry and duty cycle but investors should check the values used and whether they are applied consistently.
How should surplus energy be measured for an energy positive claim?
Surplus should be defined at a stated measurement boundary such as site meter or point of use with losses and export arrangements clearly described so that comparisons are meaningful.
What role does behaviour play in final energy outcomes?
Occupant behaviour is one of the largest sources of variance; models that do not stress test behavioural changes risk overstating surplus.
Are there standardised tools that investors can ask for?
Yes many projects use recognised dynamic simulation engines and typical meteorological year weather files; investors can ask for the software name the weather file and key input tables to assess credibility.
General information about the CurveBlock platform. Not financial, legal or tax advice. Capital is at risk. The value of digital shares can fall as well as rise. Past performance is not a guide to future returns.
