Titulo:
Monthly Forecast of Electricity Demand with Time Series
.
Guardado en:
1794-1237
2463-0950
13
2017-06-20
Revista EIA/ English version - 2017
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
| id |
metarevistapublica_eia_revistaeia_englishversion_8_article_1104 |
|---|---|
| record_format |
ojs |
| spelling |
Monthly Forecast of Electricity Demand with Time Series Monthly Forecast of Electricity Demand with Time Series The high volatility of electricity prices has motivated researchers and academics to design models that will enable the forecast of electricity demand in short, medium and long terms. This paper presents a model for forecasting the monthly electricity demand based on time series. The model uses the electricity demand values of Colombia’s National Interconnected System (NIS) for the 2008-2014 period as its base. It was concluded that the time series applied to the electricity demand forecast enable a high accuracy level of prediction of future electricity demands (GWh), information which can lead to advantages for producers, distributors and large consumers when establishing strategies, streamlining operations and reaching bilateral agreements Gil Vera, Víctor Daniel Energy markets Forecasting models Monthly electricity demand Time series 13 26 Artículo de revista Journal article 2017-06-20 00:00:00 2017-06-20 00:00:00 2017-06-20 application/pdf Revista EIA / English version Revista EIA / English version 1794-1237 2463-0950 https://revistas.eia.edu.co/index.php/Reveiaenglish/article/view/1104 https://revistas.eia.edu.co/index.php/Reveiaenglish/article/view/1104 eng https://creativecommons.org/licenses/by-nc-sa/4.0/ Revista EIA/ English version - 2017 https://revistas.eia.edu.co/index.php/Reveiaenglish/article/download/1104/1039 info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 http://purl.org/redcol/resource_type/ARTREF info:eu-repo/semantics/publishedVersion http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 Text Publication |
| institution |
UNIVERSIDAD EIA |
| thumbnail |
https://nuevo.metarevistas.org/UNIVERSIDADEIA/logo.png |
| country_str |
Colombia |
| collection |
Revista EIA / English version |
| title |
Monthly Forecast of Electricity Demand with Time Series |
| spellingShingle |
Monthly Forecast of Electricity Demand with Time Series Gil Vera, Víctor Daniel Energy markets Forecasting models Monthly electricity demand Time series |
| title_short |
Monthly Forecast of Electricity Demand with Time Series |
| title_full |
Monthly Forecast of Electricity Demand with Time Series |
| title_fullStr |
Monthly Forecast of Electricity Demand with Time Series |
| title_full_unstemmed |
Monthly Forecast of Electricity Demand with Time Series |
| title_sort |
monthly forecast of electricity demand with time series |
| description_eng |
The high volatility of electricity prices has motivated researchers and academics to design models that will enable the forecast of electricity demand in short, medium and long terms. This paper presents a model for forecasting the monthly electricity demand based on time series. The model uses the electricity demand values of Colombia’s National Interconnected System (NIS) for the 2008-2014 period as its base. It was concluded that the time series applied to the electricity demand forecast enable a high accuracy level of prediction of future electricity demands (GWh), information which can lead to advantages for producers, distributors and large consumers when establishing strategies, streamlining operations and reaching bilateral agreements
|
| author |
Gil Vera, Víctor Daniel |
| author_facet |
Gil Vera, Víctor Daniel |
| topic |
Energy markets Forecasting models Monthly electricity demand Time series |
| topic_facet |
Energy markets Forecasting models Monthly electricity demand Time series |
| citationvolume |
13 |
| citationissue |
26 |
| publisher |
Revista EIA / English version |
| ispartofjournal |
Revista EIA / English version |
| source |
https://revistas.eia.edu.co/index.php/Reveiaenglish/article/view/1104 |
| language |
eng |
| format |
Article |
| rights |
https://creativecommons.org/licenses/by-nc-sa/4.0/ Revista EIA/ English version - 2017 info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 |
| type_driver |
info:eu-repo/semantics/article |
| type_coar |
http://purl.org/coar/resource_type/c_6501 |
| type_version |
info:eu-repo/semantics/publishedVersion |
| type_coarversion |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| type_content |
Text |
| publishDate |
2017-06-20 |
| date_accessioned |
2017-06-20 00:00:00 |
| date_available |
2017-06-20 00:00:00 |
| url |
https://revistas.eia.edu.co/index.php/Reveiaenglish/article/view/1104 |
| url_doi |
https://revistas.eia.edu.co/index.php/Reveiaenglish/article/view/1104 |
| issn |
1794-1237 |
| eissn |
2463-0950 |
| url2_str_mv |
https://revistas.eia.edu.co/index.php/Reveiaenglish/article/download/1104/1039 |
| _version_ |
1811200276328087552 |