Development of Neurofuzzy Architectures for Electricity Price Forecasting
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- qyq18
- Type
- D - Journal article
- DOI
-
10.3390/en13051209
- Title of journal
- Energies
- Article number
- 1209
- First page
- -
- Volume
- 13
- Issue
- 5
- ISSN
- 1996-1073
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Electricity price forecasting is an important financial-related process for any decision-making system in power sector. Electricity Price is considered to possess more volatile characteristics than electricity load, thus the difficulty to predict it accurately. The significance of this paper is the development of a novel clustering-based neurofuzzy prediction model, utilising for the first time an asymmetric fuzzy membership and an efficient fuzzification scheme in its structure. Results revealed the advantage of the proposed methodology over existing forecasting models, indicating thus the rationale of introducing such new “family” of learning systems in the demanding area of time-series analysis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
