Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approach
Forecasting electricity demand in South Africa remains an increasingly national challenge as the government does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. Effective measures to rapidly reduce the demand of electricity are urgently n...
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| Format: | Article |
| Language: | English |
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AIMS Press
2019-12-01
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| Series: | AIMS Energy |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/10.3934/energy.2019.6.857/fulltext.html |
| _version_ | 1811344306849447936 |
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| author | Norman Maswanganyi Edmore Ranganai Caston Sigauke |
| author_facet | Norman Maswanganyi Edmore Ranganai Caston Sigauke |
| author_sort | Norman Maswanganyi |
| collection | DOAJ |
| description | Forecasting electricity demand in South Africa remains an increasingly national challenge as the government does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. Effective measures to rapidly reduce the demand of electricity are urgently needed to deal with future electricity prices and government policies uncertainties within the energy industry. Moreover, long-term peak electricity demand forecasting methods are needed to quantify the uncertainty of future electricity demand for better electricity security management. The prediction of long-term electricity demand assists decision makers in the electricity sector in planning for capacity generation. This paper presents an application of quantile regression averaging (QRA) approach using South African monthly and quarterly data ranging from January 2007 to December 2014. Variable selection is done in a comparative manner using ridge, least absolute shrinkage and selection operator (Lasso), cross validation (CV) and elastic net. We compare the forecasting accuracy of monthly peak electricity demand (MPED) and quarterly peak electricity demand (QPED) forecasting models using generalised additive models (GAMs) and QRA. The coefficient estimates for ridge, Lasso and elastic net are estimated and compared using MPED and QPED data. |
| first_indexed | 2024-04-13T19:45:20Z |
| format | Article |
| id | doaj.art-70bbd0ae282c472195dc5a89f04bf719 |
| institution | Directory Open Access Journal |
| issn | 2333-8326 2333-8334 |
| language | English |
| last_indexed | 2024-04-13T19:45:20Z |
| publishDate | 2019-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Energy |
| spelling | doaj.art-70bbd0ae282c472195dc5a89f04bf7192022-12-22T02:32:45ZengAIMS PressAIMS Energy2333-83262333-83342019-12-017685788210.3934/energy.2019.6.857Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approachNorman Maswanganyi0Edmore Ranganai1Caston Sigauke21 Department of Statistics and Operations Research, University of Limpopo, Private Bag X1106, Sovenga, 0727, South Africa2 Department of Statistics, University of South Africa, Private Bag X6, Florida 1710, South Africa3 Department of Statistics, University of Venda, Private Bag X5050, Thohoyandou, 0950, Limpopo, South AfricaForecasting electricity demand in South Africa remains an increasingly national challenge as the government does not sufficiently take into account the impact of the electricity prices in their electricity demand forecast. Effective measures to rapidly reduce the demand of electricity are urgently needed to deal with future electricity prices and government policies uncertainties within the energy industry. Moreover, long-term peak electricity demand forecasting methods are needed to quantify the uncertainty of future electricity demand for better electricity security management. The prediction of long-term electricity demand assists decision makers in the electricity sector in planning for capacity generation. This paper presents an application of quantile regression averaging (QRA) approach using South African monthly and quarterly data ranging from January 2007 to December 2014. Variable selection is done in a comparative manner using ridge, least absolute shrinkage and selection operator (Lasso), cross validation (CV) and elastic net. We compare the forecasting accuracy of monthly peak electricity demand (MPED) and quarterly peak electricity demand (QPED) forecasting models using generalised additive models (GAMs) and QRA. The coefficient estimates for ridge, Lasso and elastic net are estimated and compared using MPED and QPED data.https://www.aimspress.com/article/10.3934/energy.2019.6.857/fulltext.htmladditive quantile regressioncubic smoothing splineslong term peak demand forecastingpenalised regression methodsquantile regression averaging modeltemperature |
| spellingShingle | Norman Maswanganyi Edmore Ranganai Caston Sigauke Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approach AIMS Energy additive quantile regression cubic smoothing splines long term peak demand forecasting penalised regression methods quantile regression averaging model temperature |
| title | Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approach |
| title_full | Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approach |
| title_fullStr | Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approach |
| title_full_unstemmed | Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approach |
| title_short | Long-term peak electricity demand forecasting in South Africa: A quantile regression averaging approach |
| title_sort | long term peak electricity demand forecasting in south africa a quantile regression averaging approach |
| topic | additive quantile regression cubic smoothing splines long term peak demand forecasting penalised regression methods quantile regression averaging model temperature |
| url | https://www.aimspress.com/article/10.3934/energy.2019.6.857/fulltext.html |
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