Abstract
Renewable energy technologies (RETs) have attracted significant public attention for several reasons, the most important being that they are clean alternative energy sources that help reduce greenhouse gas emissions. To increase the probability that RETs will be successful, it is essential to reduce the uncertainty about its adoption with accurate long-term demand forecasting. This study develops a diffusion model that incorporates the effect of competitive interrelationships among renewable sources to forecast the growth pattern of five RETs: solar photovoltaic, wind power, and fuel cell in the electric power sector, and solar thermal and geothermal energy in the heating sector. The 2-step forecasting procedure is based on the Bayus, (1993. Manage. Sci. 39, 11, 1319-1333) price function and a diffusion model suggested by Hahn et al. (1994. Marketing Sci. 13, 3, 224-247). In an empirical analysis, the model is applied to the South Korean renewable energy market.
| Original language | English |
|---|---|
| Pages (from-to) | 248-257 |
| Number of pages | 10 |
| Journal | Energy Policy |
| Volume | 69 |
| DOIs | |
| State | Published - Jun 2014 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy -
SDG 13 Climate Action
Keywords
- Competitive interrelationship
- Demand forecasting
- Innovation diffusion model
- Renewable energy technology
- South Korea
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