Forecasting Electric Load Demand through Advanced Statistical Techniques
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Authors
Silva, JesúsSenior Naveda, Alexa
García Guliany, Jesús
Niebles Núẽz, William
Hernández Palma, Hugo
Issue Date
2020-01-07Keywords
Bayesian networksDecision making
Bayesian methods
Colombia
Electric load demands
Forecasting models
Statistical techniques
Electric load forecasting
Metadata
Show full item recordPublisher
Institute of Physics PublishingJournal
Journal of Physics: Conference SeriesDOI
10.1088/1742-6596/1432/1/012031Additional Links
https://iopscience.iop.org/article/10.1088/1742-6596/1432/1/012031Abstract
Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/openAccessAttribution-NonCommercial-ShareAlike 4.0 International
Language
engISSN
17426588EISSN
17426596ae974a485f413a2113503eed53cd6c53
10.1088/1742-6596/1432/1/012031
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The following license files are associated with this item:
- Creative Commons