A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling
Abstract The short‐term electricity consumption forecasting can help to ensure the safe and reliable operation of the power system. Power companies usually need to report the electricity consumption of the current month five to seven days in advance and make a power generation plan for the next mont...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-05-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12409 |
| _version_ | 1798036219644346368 |
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| author | Minjie Wei Mi Wen Junran Luo |
| author_facet | Minjie Wei Mi Wen Junran Luo |
| author_sort | Minjie Wei |
| collection | DOAJ |
| description | Abstract The short‐term electricity consumption forecasting can help to ensure the safe and reliable operation of the power system. Power companies usually need to report the electricity consumption of the current month five to seven days in advance and make a power generation plan for the next month. The existing studies are usually lack of appropriate feature selection methods and hard to achieve satisfactory results. This paper proposes a short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling. The maximum information coefficient (MIC) is employed to analyse the feature correlation, the electricity consumption curves are converted to several sub‐sequences of different frequency bands by the variational mode decomposition (VMD) to describe signal characteristics accurately, a hybrid model based on bidirectional gated recurrent unit (BiGRU) is innovated to extract the temporal and spatial features of the data and capture the contextual information from the complete time series, attention mechanism is used to do extract useful information and assign weights to make forecast. Compared with several benchmark methods, the proposed approach achieves better electricity consumption curve fitting and higher forecasting accuracy with the increase of forecasting step size. |
| first_indexed | 2024-04-11T21:09:31Z |
| format | Article |
| id | doaj.art-3c9066104bcd47fc92e5e8f562d6e6f3 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-04-11T21:09:31Z |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-3c9066104bcd47fc92e5e8f562d6e6f32022-12-22T04:03:04ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952022-05-0116102003201510.1049/gtd2.12409A short‐term electricity consumption forecasting approach based on feature processing and hybrid modellingMinjie Wei0Mi Wen1Junran Luo2College of Electrical Engineering Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Computer Science and Technology Shanghai University of Electric Power Shanghai People's Republic of ChinaCollege of Computer Science and Technology Shanghai University of Electric Power Shanghai People's Republic of ChinaAbstract The short‐term electricity consumption forecasting can help to ensure the safe and reliable operation of the power system. Power companies usually need to report the electricity consumption of the current month five to seven days in advance and make a power generation plan for the next month. The existing studies are usually lack of appropriate feature selection methods and hard to achieve satisfactory results. This paper proposes a short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling. The maximum information coefficient (MIC) is employed to analyse the feature correlation, the electricity consumption curves are converted to several sub‐sequences of different frequency bands by the variational mode decomposition (VMD) to describe signal characteristics accurately, a hybrid model based on bidirectional gated recurrent unit (BiGRU) is innovated to extract the temporal and spatial features of the data and capture the contextual information from the complete time series, attention mechanism is used to do extract useful information and assign weights to make forecast. Compared with several benchmark methods, the proposed approach achieves better electricity consumption curve fitting and higher forecasting accuracy with the increase of forecasting step size.https://doi.org/10.1049/gtd2.12409ReliabilityInterpolation and function approximation (numerical analysis)Signal processing and detectionPower system management, operation and economicsPower system planning and layoutInterpolation and function approximation (numerical analysis) |
| spellingShingle | Minjie Wei Mi Wen Junran Luo A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling IET Generation, Transmission & Distribution Reliability Interpolation and function approximation (numerical analysis) Signal processing and detection Power system management, operation and economics Power system planning and layout Interpolation and function approximation (numerical analysis) |
| title | A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling |
| title_full | A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling |
| title_fullStr | A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling |
| title_full_unstemmed | A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling |
| title_short | A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling |
| title_sort | short term electricity consumption forecasting approach based on feature processing and hybrid modelling |
| topic | Reliability Interpolation and function approximation (numerical analysis) Signal processing and detection Power system management, operation and economics Power system planning and layout Interpolation and function approximation (numerical analysis) |
| url | https://doi.org/10.1049/gtd2.12409 |
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