Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast
[Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhen ZHANG [verfasserIn] Ying XIAO [verfasserIn] Yongjian REN [verfasserIn] Zhenghong CHEN [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch ; Chinesisch |
| Erschienen: |
2024 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
In: 南方能源建设 - Energy Observer Magazine Co., Ltd., 2021, 11(2024), 1, Seite 122-132 |
|---|---|
| Übergeordnetes Werk: |
volume:11 ; year:2024 ; number:1 ; pages:122-132 |
| Links: |
|---|
| DOI / URN: |
10.16516/j.ceec.2024.1.13 |
|---|
| Katalog-ID: |
DOAJ098509241 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ098509241 | ||
| 003 | DE-627 | ||
| 005 | 20240413225804.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240413s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.16516/j.ceec.2024.1.13 |2 doi | |
| 035 | |a (DE-627)DOAJ098509241 | ||
| 035 | |a (DE-599)DOAJb61af00c74694772a534226c469b576b | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng |a chi | ||
| 050 | 0 | |a HD9502-9502.5 | |
| 100 | 0 | |a Zhen ZHANG |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a [Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. | ||
| 650 | 4 | |a ensemble empirical mode decomposition | |
| 650 | 4 | |a electricity consumption | |
| 650 | 4 | |a meteorological factors | |
| 650 | 4 | |a refined forecast | |
| 650 | 4 | |a regression model | |
| 653 | 0 | |a Energy industries. Energy policy. Fuel trade | |
| 700 | 0 | |a Ying XIAO |e verfasserin |4 aut | |
| 700 | 0 | |a Yongjian REN |e verfasserin |4 aut | |
| 700 | 0 | |a Zhenghong CHEN |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t 南方能源建设 |d Energy Observer Magazine Co., Ltd., 2021 |g 11(2024), 1, Seite 122-132 |w (DE-627)DOAJ078619238 |x 20958676 |7 nnns |
| 773 | 1 | 8 | |g volume:11 |g year:2024 |g number:1 |g pages:122-132 |
| 856 | 4 | 0 | |u https://doi.org/10.16516/j.ceec.2024.1.13 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/b61af00c74694772a534226c469b576b |z kostenfrei |
| 856 | 4 | 0 | |u https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13 |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/2095-8676 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 951 | |a AR | ||
| 952 | |d 11 |j 2024 |e 1 |h 122-132 | ||
| author_variant |
z z zz y x yx y r yr z c zc |
|---|---|
| matchkey_str |
article:20958676:2024----::plctooembmtobsdneerlgclatrigieeti |
| hierarchy_sort_str |
2024 |
| callnumber-subject-code |
HD |
| publishDate |
2024 |
| allfields |
10.16516/j.ceec.2024.1.13 doi (DE-627)DOAJ098509241 (DE-599)DOAJb61af00c74694772a534226c469b576b DE-627 ger DE-627 rakwb eng chi HD9502-9502.5 Zhen ZHANG verfasserin aut Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier [Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. ensemble empirical mode decomposition electricity consumption meteorological factors refined forecast regression model Energy industries. Energy policy. Fuel trade Ying XIAO verfasserin aut Yongjian REN verfasserin aut Zhenghong CHEN verfasserin aut In 南方能源建设 Energy Observer Magazine Co., Ltd., 2021 11(2024), 1, Seite 122-132 (DE-627)DOAJ078619238 20958676 nnns volume:11 year:2024 number:1 pages:122-132 https://doi.org/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/article/b61af00c74694772a534226c469b576b kostenfrei https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/toc/2095-8676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 11 2024 1 122-132 |
| spelling |
10.16516/j.ceec.2024.1.13 doi (DE-627)DOAJ098509241 (DE-599)DOAJb61af00c74694772a534226c469b576b DE-627 ger DE-627 rakwb eng chi HD9502-9502.5 Zhen ZHANG verfasserin aut Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier [Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. ensemble empirical mode decomposition electricity consumption meteorological factors refined forecast regression model Energy industries. Energy policy. Fuel trade Ying XIAO verfasserin aut Yongjian REN verfasserin aut Zhenghong CHEN verfasserin aut In 南方能源建设 Energy Observer Magazine Co., Ltd., 2021 11(2024), 1, Seite 122-132 (DE-627)DOAJ078619238 20958676 nnns volume:11 year:2024 number:1 pages:122-132 https://doi.org/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/article/b61af00c74694772a534226c469b576b kostenfrei https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/toc/2095-8676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 11 2024 1 122-132 |
| allfields_unstemmed |
10.16516/j.ceec.2024.1.13 doi (DE-627)DOAJ098509241 (DE-599)DOAJb61af00c74694772a534226c469b576b DE-627 ger DE-627 rakwb eng chi HD9502-9502.5 Zhen ZHANG verfasserin aut Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier [Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. ensemble empirical mode decomposition electricity consumption meteorological factors refined forecast regression model Energy industries. Energy policy. Fuel trade Ying XIAO verfasserin aut Yongjian REN verfasserin aut Zhenghong CHEN verfasserin aut In 南方能源建设 Energy Observer Magazine Co., Ltd., 2021 11(2024), 1, Seite 122-132 (DE-627)DOAJ078619238 20958676 nnns volume:11 year:2024 number:1 pages:122-132 https://doi.org/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/article/b61af00c74694772a534226c469b576b kostenfrei https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/toc/2095-8676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 11 2024 1 122-132 |
| allfieldsGer |
10.16516/j.ceec.2024.1.13 doi (DE-627)DOAJ098509241 (DE-599)DOAJb61af00c74694772a534226c469b576b DE-627 ger DE-627 rakwb eng chi HD9502-9502.5 Zhen ZHANG verfasserin aut Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier [Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. ensemble empirical mode decomposition electricity consumption meteorological factors refined forecast regression model Energy industries. Energy policy. Fuel trade Ying XIAO verfasserin aut Yongjian REN verfasserin aut Zhenghong CHEN verfasserin aut In 南方能源建设 Energy Observer Magazine Co., Ltd., 2021 11(2024), 1, Seite 122-132 (DE-627)DOAJ078619238 20958676 nnns volume:11 year:2024 number:1 pages:122-132 https://doi.org/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/article/b61af00c74694772a534226c469b576b kostenfrei https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/toc/2095-8676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 11 2024 1 122-132 |
| allfieldsSound |
10.16516/j.ceec.2024.1.13 doi (DE-627)DOAJ098509241 (DE-599)DOAJb61af00c74694772a534226c469b576b DE-627 ger DE-627 rakwb eng chi HD9502-9502.5 Zhen ZHANG verfasserin aut Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier [Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. ensemble empirical mode decomposition electricity consumption meteorological factors refined forecast regression model Energy industries. Energy policy. Fuel trade Ying XIAO verfasserin aut Yongjian REN verfasserin aut Zhenghong CHEN verfasserin aut In 南方能源建设 Energy Observer Magazine Co., Ltd., 2021 11(2024), 1, Seite 122-132 (DE-627)DOAJ078619238 20958676 nnns volume:11 year:2024 number:1 pages:122-132 https://doi.org/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/article/b61af00c74694772a534226c469b576b kostenfrei https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13 kostenfrei https://doaj.org/toc/2095-8676 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 11 2024 1 122-132 |
| language |
English Chinese |
| source |
In 南方能源建设 11(2024), 1, Seite 122-132 volume:11 year:2024 number:1 pages:122-132 |
| sourceStr |
In 南方能源建设 11(2024), 1, Seite 122-132 volume:11 year:2024 number:1 pages:122-132 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
ensemble empirical mode decomposition electricity consumption meteorological factors refined forecast regression model Energy industries. Energy policy. Fuel trade |
| isfreeaccess_bool |
true |
| container_title |
南方能源建设 |
| authorswithroles_txt_mv |
Zhen ZHANG @@aut@@ Ying XIAO @@aut@@ Yongjian REN @@aut@@ Zhenghong CHEN @@aut@@ |
| publishDateDaySort_date |
2024-01-01T00:00:00Z |
| hierarchy_top_id |
DOAJ078619238 |
| id |
DOAJ098509241 |
| language_de |
englisch chinesisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ098509241</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413225804.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.16516/j.ceec.2024.1.13</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ098509241</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJb61af00c74694772a534226c469b576b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield><subfield code="a">chi</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HD9502-9502.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Zhen ZHANG</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">[Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ensemble empirical mode decomposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">electricity consumption</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">meteorological factors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">refined forecast</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">regression model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Energy industries. Energy policy. Fuel trade</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ying XIAO</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yongjian REN</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhenghong CHEN</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">南方能源建设</subfield><subfield code="d">Energy Observer Magazine Co., Ltd., 2021</subfield><subfield code="g">11(2024), 1, Seite 122-132</subfield><subfield code="w">(DE-627)DOAJ078619238</subfield><subfield code="x">20958676</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:122-132</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.16516/j.ceec.2024.1.13</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/b61af00c74694772a534226c469b576b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2095-8676</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="h">122-132</subfield></datafield></record></collection>
|
| callnumber-first |
H - Social Science |
| author |
Zhen ZHANG |
| spellingShingle |
Zhen ZHANG misc HD9502-9502.5 misc ensemble empirical mode decomposition misc electricity consumption misc meteorological factors misc refined forecast misc regression model misc Energy industries. Energy policy. Fuel trade Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast |
| authorStr |
Zhen ZHANG |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)DOAJ078619238 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
DOAJ |
| remote_str |
true |
| callnumber-label |
HD9502-9502 |
| illustrated |
Not Illustrated |
| issn |
20958676 |
| topic_title |
HD9502-9502.5 Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast ensemble empirical mode decomposition electricity consumption meteorological factors refined forecast regression model |
| topic |
misc HD9502-9502.5 misc ensemble empirical mode decomposition misc electricity consumption misc meteorological factors misc refined forecast misc regression model misc Energy industries. Energy policy. Fuel trade |
| topic_unstemmed |
misc HD9502-9502.5 misc ensemble empirical mode decomposition misc electricity consumption misc meteorological factors misc refined forecast misc regression model misc Energy industries. Energy policy. Fuel trade |
| topic_browse |
misc HD9502-9502.5 misc ensemble empirical mode decomposition misc electricity consumption misc meteorological factors misc refined forecast misc regression model misc Energy industries. Energy policy. Fuel trade |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
南方能源建设 |
| hierarchy_parent_id |
DOAJ078619238 |
| hierarchy_top_title |
南方能源建设 |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)DOAJ078619238 |
| title |
Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast |
| ctrlnum |
(DE-627)DOAJ098509241 (DE-599)DOAJb61af00c74694772a534226c469b576b |
| title_full |
Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast |
| author_sort |
Zhen ZHANG |
| journal |
南方能源建设 |
| journalStr |
南方能源建设 |
| callnumber-first-code |
H |
| lang_code |
eng chi |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2024 |
| contenttype_str_mv |
txt |
| container_start_page |
122 |
| author_browse |
Zhen ZHANG Ying XIAO Yongjian REN Zhenghong CHEN |
| container_volume |
11 |
| class |
HD9502-9502.5 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Zhen ZHANG |
| doi_str_mv |
10.16516/j.ceec.2024.1.13 |
| author2-role |
verfasserin |
| title_sort |
application of eemd-bp method based on meteorological factors in grid electricity consumption forecast |
| callnumber |
HD9502-9502.5 |
| title_auth |
Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast |
| abstract |
[Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. |
| abstractGer |
[Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. |
| abstract_unstemmed |
[Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ |
| container_issue |
1 |
| title_short |
Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast |
| url |
https://doi.org/10.16516/j.ceec.2024.1.13 https://doaj.org/article/b61af00c74694772a534226c469b576b https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13 https://doaj.org/toc/2095-8676 |
| remote_bool |
true |
| author2 |
Ying XIAO Yongjian REN Zhenghong CHEN |
| author2Str |
Ying XIAO Yongjian REN Zhenghong CHEN |
| ppnlink |
DOAJ078619238 |
| callnumber-subject |
HD - Industries, Land Use, Labor |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.16516/j.ceec.2024.1.13 |
| callnumber-a |
HD9502-9502.5 |
| up_date |
2024-07-03T17:40:10.350Z |
| _version_ |
1803580510412210176 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ098509241</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413225804.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.16516/j.ceec.2024.1.13</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ098509241</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJb61af00c74694772a534226c469b576b</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield><subfield code="a">chi</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">HD9502-9502.5</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Zhen ZHANG</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of EEMD-BP Method Based on Meteorological Factors in Grid Electricity Consumption Forecast</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">[Introduction] The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. [Method] The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. [Result] This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. [Conclusion] The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ensemble empirical mode decomposition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">electricity consumption</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">meteorological factors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">refined forecast</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">regression model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Energy industries. Energy policy. Fuel trade</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ying XIAO</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yongjian REN</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zhenghong CHEN</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">南方能源建设</subfield><subfield code="d">Energy Observer Magazine Co., Ltd., 2021</subfield><subfield code="g">11(2024), 1, Seite 122-132</subfield><subfield code="w">(DE-627)DOAJ078619238</subfield><subfield code="x">20958676</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:122-132</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.16516/j.ceec.2024.1.13</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/b61af00c74694772a534226c469b576b</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.energychina.press/en/article/doi/10.16516/j.ceec.2024.1.13</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2095-8676</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="h">122-132</subfield></datafield></record></collection>
|
| score |
7.399935 |