Agent-based simulation models are an important tool
to study the effectiveness of policy interventions on the uptake of
residential photovoltaic systems by households, a cornerstone of
sustainable energy system transition. In order for these models
to be trustworthy, they require rigorous validation.
However, the canonical approach of validating emulation models
through calibration with parameters that minimize the difference
of model results and reference data fails when the model is
subject to many stochastic influences. The residential photovoltaic
diffusion model PVact features numerous stochastic influences
that prevent straightforward optimization-driven calibration.
From the analysis of the results of a case-study on the cities
Dresden and Leipzig (Germany) based on three error metrics
(mean average error, root mean square error and cumulative
average error), this research identifies a parameter range where
stochastic fluctuations exceed differences between results of
different parameterization and a minimization-based calibration
approach fails.
Based on this observation, an approach is developed that
aggregates model behavior across multiple simulation runs and
parameter combinations to compare results between scenarios
representing different future developments or policy interventions
of interest.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87485 |
| Date | 19 October 2023 |
| Creators | Johanning, Simon, Abitz, Daniel, Schulte, Emily, Scheller, Fabian, Bruckner, Thomas |
| Publisher | IEEE |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1109/EEM54602.2022.9921039 |
Page generated in 0.002 seconds