Forecasting of electricity costs based on an enhanced gray-based learning model: A case study of renewable energy in Taiwan

Shun Chung Lee, Li Hsing Shih

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

This work presents a novel gray-based cost efficiency (GCE) model that integrates the gray forecasting model into a two-factor cost efficiency curve model for renewable energy (RE) technologies and identifies the optimal forecasting model for power generation cost of RE technologies. The analytical framework of proposed GCE model improves short-term prediction of power generation cost, and can be applied during the early developmental stages for RE technologies. Empirical analysis is based on wind power data for Taiwan. Time lag of knowledge stock was simulated to represent the actual relationship between R&D expenditures and cost reductions in power generation by knowledge stock. Analytical results demonstrate the GCE model is a useful tool to quantify the influences of cost reductions in power generation. The implications of analytical results are that institutional policy instruments play an important role in RE technologies achieving cost reductions and market adoption. The proposed GCE model can be applied to all high-technology cases, and particularly to RE technologies. The study concludes by outlining the limitations of the proposed GCE model and directions for further research.

Original languageEnglish
Pages (from-to)1242-1253
Number of pages12
JournalTechnological Forecasting and Social Change
Volume78
Issue number7
DOIs
Publication statusPublished - 2011 Sep 1

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Applied Psychology
  • Management of Technology and Innovation

Fingerprint Dive into the research topics of 'Forecasting of electricity costs based on an enhanced gray-based learning model: A case study of renewable energy in Taiwan'. Together they form a unique fingerprint.

Cite this