This article reviews the calculation models and their basic principles related to energy consumption and efficiency as well as climate risks.
Energy consumption calculations
Energy consumption refers to the energy that the building's occupants and functions use to satisfy the building's energy needs.
It includes energy used for heating, cooling, lighting, appliances and other building heating systems (with different efficiencies) using different types of fuels.
The model takes into account the properties of the property. These characteristics include building type, year of construction, materials and location.
Energy performance model
SkenarioLabs' energy efficiency certificate model uses the building's consumption data and energy classes to determine energy efficiency (energy class and e-number).
The model is made using statistical models. The model takes into account the characteristics of the property in a localized manner. These characteristics include building type, year of construction and location. In addition, the model takes into account the consumption of primary energy.
- The energy efficiency class of the building is described by codes A–G.
- The building's energy classification is based on the calculated energy efficiency benchmark, or E-number (kWh/m2/year).
- Based on the E number, the buildings are divided into the top 15% and top 30% best in Finland.
If an energy certificate can be found for the object, it is automatically retrieved from the Energy Certificate Register. If the energy certificate is known, it should still be reported.
Flood risk ja VaR (value at risk)
- Typically, about 2% of the collateral can be found for flood risk.
- The probabilities of flood risks are indicated according to the estimated frequency, e.g. once every 2 years (1/2y). Values up to 1/1000v.
- The type of flood is indicated according to the most likely flood type (coastal or marine flood).
VaR (value at risk) tells you how much the building's repair costs due to floods will be in the worst case over a 20-year period.
In order to get a picture of the flood risk of the review period and its distribution, we simulated flood events for the review site using their annual probabilities. For example, in 1/100 years, a flood will occur within a year with a 1% probability. We repeat the simulation 10,000 times to produce a distribution of costs and also to get an idea of the effect of less frequent floods on expected costs.
The costs are based on typical flood repair costs. The cost caused by an individual flood event depends on the water level, the characteristics of the building and the extent of the flood.
VaR is stated both as a total figure and €/m2, so that it is possible to relate the value to the object's market value.