The core of the tool is a nested logit approach, which optimizes objectives of “agents” under imperfect information conditions and by that represents the decisions maker concerning building related decisions.
Technical implementation: Python code
By using a Weibul distribution, those buildings and components are identified, which have to be replaced or abolished. An agent specific, nested logit approach combined with a logistic diffusion curve models models the choice between technologies (renovation measures, HVAC systems). By this approach, Invert/EE-Lab models the decision making of agents (i.e. building owner types) regarding building renovation and space heating, hot water and cooling systems. Policy instruments, such as economic incentives, regulatory instruments, information, advices may affect these decisions.
Taking into account regional climate data, a standard static monthly balance approach calculates energy needs and delivered as well as final energy demand.
Invert/EE-Lab models the contribution of PV to electricity consumption (i) for appliances, (ii) for heating and hot water and (iii) for export to the grid. The model simulates building owner’s decisions regarding the choice for a PV system and the size of installed PV collectors.
The module for the cooling energy demand in addition simulates the diffusion of cooling devices in buildings.
The lighting module focuses on a vintage model based on regulatory instruments and technological progress without endogenous modelling of investment decisions.
Standard outputs from the Invert/EE-Lab on an annual basis are:
Moreover, Invert/EE-Lab offers the possibility to derive more detailed and other type of result evaluations as well. Based on the needs of the policy processes we will have to discuss which other type of evaluations of the result data set might be required.