ADVANCES IN ENERGY FORECASTING MODELS BASED ON ENGINEERING ECONOMICS
By Ernst Worrell, S. Ramesohl, G. Boyd. 2004
143 citations recorded by the source corpus.
Subfields: Finance, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/f3f75e262388fd524fbacd0777eea4d0b6bce31e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
In Renewable Energy, Sustainability and the Environment
- Simon Hirzel
- T. Fleiter
- G. Timilsina
- S. Bhattacharyya
- Ernst Worrell
- Diego García-Gusano
- Wina Crijns-Graus
- Martin Kirkengen
Citations in the corpus
- Co-benefits of energy efficiency improvement and air pollution abatement in the Chinese iron and steel industry (2014, background): “4%), especially for long-term issues, like climate change [17,37,65].”
- Classification and challenges of bottom-up energy system models - A review (2020, background): “[14] in 2004 differentiated bottom-up energy system models based on”
- Energy demand models for policy formulation: a comparative study of energy demand models (2009, background): “The data gap poses hurdles to build scenarios, evaluate technologies and analyze policy impacts (Worrel et al. 2004).”
- The characteristics of energy-efficiency measures – a neglected dimension (2012, methodology): “Including more realistic adoption behavior in the models used is expected to significantly improve the value of the modeling results, but requires EEM characteristics to be considered (Fleiter et al. 2011; Worrell et al. 2004).”
- The role of the discount rates in energy systems optimisation models (2016, background): “[16], these approaches lack thorough understanding of the relevant barriers and their 4 effect on technology adoption.”
- Decarbonising ceramic manufacturing: A techno-economic analysis of energy efficient sintering technologies in the functional materials sector (2019, methodology): “Generally speaking, two approaches namely prescriptive approach (or social perspective) and descriptive approach (or industry perspective) are used to guide the choice of the discount factor [195].”
- A review of energy system models (2010, background): “The data gap poses hurdles to build scenarios, evaluate technologies and analyse policy impacts ( Worrel et al. , 2004”