Forecasting Energy Consumption Analysis and Simulation Tool (FORECAST)
TEP GmbH, Fraunhofer ISI and Institute for ResourceEfficiency and Energy Strategies (IREES)
Martin Jakob, TEP Energy GmbH
Proprietary
SURE - WP 4, 14 and 15
The FORECAST modelling platform aims to develop long-term scenarios for future energy demand of individual countries and world regions until 2050. It is based on a bottom-up modelling approach considering the dynamics of technologies and socio-economic drivers. The model allows to address various research questions related to energy demand including scenarios for the future demand of individual energy carriers like electricity or natural gas, calculating energy saving potentials and the impact on greenhouse gas (GHG) emissions as well as abatement cost curves and ex-ante policy impact assessments.
Features
- Bottom-up modelling of demand
- Four individual modules: industry, residential, services, transport
- Actors heterogeneity in technology diffusion
- Logit-approach considering the total cost of ownership plus other intangible costs
- Development of new energy demand drivers and appliances (incl. their load profile and material demand).
- Represent the energy efficiency gap and decarbonization gap in the modelling and the related uncertainties for decisions in respective investments in the building sector.
- Assess efficiency measures and demand projections in terms of their robustness towards achieving efficiency improvements and provide insights on the uncertainty level of such achievements for the buildings sector
Facts
| Class | |
| Type | Deterministic + Monte Carlo |
| Spatial regions | European countries and Switzerland |
| Spatial resolution | National or sub-regional resolution depending on the case study. |
| Time coverage | 2020-2050 |
| Time resolution | Hourly |
Sectors | Residential, Industry, Services, Agriculture and Transport |
Category | Inputs | Outputs |
|---|---|---|
| Socioeconomy | GDP Economy wide consumption Climate policy measures Energy policy measures (subsidies) Managerial (strategical, business models) Psychological (revealed or stated preferences, willingness to pay, intentions) Sociodemographic (household, age, income, gender..) Legal | Physical production Number of employees Number of households |
| Infrastructure | ||
| Environment | GHG emissions from residential and services sectors CO2 emissions in industry Other GHGs | |
| Energy demand | Space heating Space Cooling Industrial heating Industrial cooling Hot water Electricity - appliances | |
| Energy supply/production | Space heating Space cooling Industrial heating Industrial cooling Heat storage Hot water Electricity - production Electricity - storage Electricity - installed capacity Passenger mobility Freight mobility Energy savings options per sector | |
| Resource potential | Renewable resources potentials relevant for the end-use sectors, e.g., roof-top solar PV | |
| Direct demand of resources | ||
| Trade | Sector specific retail prices | |
| Technologies Inv: Investment costs Eff: Efficiency OM: Operation and Maintenance costs LCA: Life cycle assessment indicators | CHPs (Inv,Eff,OM) Cooling (Inv,Eff,OM) Heat production - Heat pumps (Inv,Eff,OM) Heat production - Thermal solar (Inv,Eff,OM) Heat production - Boilers (Inv,Eff,OM) Storage - Heat (Inv,Eff,OM) Storage - Cold (Inv,Eff,OM) Storage - Electricity (Inv,Eff,OM) | |
| Prices | ||
| Others |
References
- Model webpage. . https://www.forecast-model.eu/forecast-en/index.php
- Kuehnbach, M.; Stute, J.; Klingler, A.-L. (2021): Impacts of avalanche effects of price-optimized electric vehicle charging. https://authors.elsevier.com/sd/article/S2211-467X(20)30161-9
- Does demand response make it worse? In: Energy Strategy Reviews 2021, 34, 100608. https://doi.org/10.1016/j.esr.2020.100608
This page was last modified on 2022.06.08, 20:04