Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan
In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis o...
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2023
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| author | Iftikhar, Hasnain Bibi, Nadeela Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_browse | Bibi, Nadeela Canas Rodrigues, Paulo Iftikhar, Hasnain López Gonzales, Javier Linkolk |
| author_facet | Iftikhar, Hasnain Bibi, Nadeela Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/429 |
| institution | UPeU |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
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| spelling | oai:cris.upeu.edu.pe:20.500.15037/4292026-10-30T16:14:37ZMultiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in PakistanIftikhar, HasnainBibi, NadeelaCanas Rodrigues, PauloLópez Gonzales, Javier Linkolkelectricity consumptionmonthly forecastingdecomposition methodstimes series modelsIn today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features.Articlehttps://www.mdpi.com/1996-1073/16/6/2579/pdf?version=1678348118https://doi.org/10.3390/en1606257910.3390/en16062579https://hdl.handle.net/20.500.15037/429W4323664655enEnergies1996-10732023-03-092026-06-13T08:13:24Z2026-06-13T08:13:24Z |
| spellingShingle | electricity consumption monthly forecasting decomposition methods times series models Iftikhar, Hasnain Bibi, Nadeela Canas Rodrigues, Paulo López Gonzales, Javier Linkolk Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan |
| title | Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan |
| title_full | Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan |
| title_fullStr | Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan |
| title_full_unstemmed | Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan |
| title_short | Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan |
| title_sort | multiple novel decomposition techniques for time series forecasting application to monthly forecasting of electricity consumption in pakistan |
| topic | electricity consumption monthly forecasting decomposition methods times series models |
| topic_facet | electricity consumption monthly forecasting decomposition methods times series models |
| url | https://www.mdpi.com/1996-1073/16/6/2579/pdf?version=1678348118 https://doi.org/10.3390/en16062579 https://hdl.handle.net/20.500.15037/429 |
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