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Structural Combination of Seasonal Exponential Smoothing Forecasts Applied to Load Forecasting
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- This article draws from research on ensembles in computational intelligence to propose structural combinations of forecasts, which are point forecast combinations that are based on information from the parameters of the individual models that generated the forecasts. Two types of structural combination are proposed which use seasonal exponential smoothing as base models, and are applied to forecast short-term electricity demand. Although forecasting performance may depend on how ensembles are generated, results show that the proposed combinations can outperform competitive benchmarks. The methods can be used to forecast other seasonal data and be extended to different types of forecasting models.
- Subjects :
- 050210 logistics & transportation
021103 operations research
Information Systems and Management
General Computer Science
Computer science
Load forecasting
05 social sciences
Exponential smoothing
0211 other engineering and technologies
Computational intelligence
02 engineering and technology
Management Science and Operations Research
Base (topology)
Industrial and Manufacturing Engineering
Modeling and Simulation
0502 economics and business
Econometrics
HD28
Physics::Atmospheric and Oceanic Physics
Subjects
Details
- Language :
- English
- ISSN :
- 03772217
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....5bd16a7c8598bcb311a6d3a298408e62