Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing
This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand pro...
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| Format: | Journal article |
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2003
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| _version_ | 1797073731559882752 |
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| author | Taylor, J |
| author_facet | Taylor, J |
| author_sort | Taylor, J |
| collection | OXFORD |
| description | This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt-Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt-Winters method outperform those from traditional Holt-Winters and from a well-specified multiplicative double seasonal ARIMA model. |
| first_indexed | 2024-03-06T23:26:13Z |
| format | Journal article |
| id | oxford-uuid:6a76b9c8-c131-4d83-ae73-97521261034b |
| institution | University of Oxford |
| last_indexed | 2024-03-06T23:26:13Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | oxford-uuid:6a76b9c8-c131-4d83-ae73-97521261034b2022-03-26T18:57:36ZShort-Term Electricity Demand Forecasting Using Double Seasonal Exponential SmoothingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6a76b9c8-c131-4d83-ae73-97521261034bSaïd Business School - Eureka2003Taylor, JThis paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the demand on the corresponding day of adjacent weeks. There is strong appeal in using a forecasting method that is able to capture both seasonalities. The multiplicative seasonal ARIMA model has been adapted for this purpose. In this paper, we adapt the Holt-Winters exponential smoothing formulation so that it can accommodate two seasonalities. We correct for residual autocorrelation using a simple autoregressive model. The forecasts produced by the new double seasonal Holt-Winters method outperform those from traditional Holt-Winters and from a well-specified multiplicative double seasonal ARIMA model. |
| spellingShingle | Taylor, J Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing |
| title | Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing |
| title_full | Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing |
| title_fullStr | Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing |
| title_full_unstemmed | Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing |
| title_short | Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing |
| title_sort | short term electricity demand forecasting using double seasonal exponential smoothing |
| work_keys_str_mv | AT taylorj shorttermelectricitydemandforecastingusingdoubleseasonalexponentialsmoothing |