Ensemble Forecasting Applied to Power Systems

Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Bracale, Antonio, Falco, Pasquale De
Format: Buch
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Bracale, Antonio
Falco, Pasquale De
description Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.
doi_str_mv 10.3390/books978-3-03928-313-2
format Book
fullrecord <record><control><sourceid>oapen</sourceid><recordid>TN_cdi_oapen_doabooks_46473</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>46473</sourcerecordid><originalsourceid>FETCH-LOGICAL-f4341-7d411c5a86d0e1cded017958ba8ddae001da90f96fd1aef0b4d2cf2f9adb60013</originalsourceid><addsrcrecordid>eNotj91KAzEQRgMiqG2fQJB9gegkk_3JZSmtCgWF6nWZ7Eykut0szYL49i7Vq8PhgwOfUncG7hE9PISUvrKvG40a0NuJBrW9UDd41smaK7XI-RMArIe6rPBa6XWf5Rg6KTbpJC3l8dB_FMth6A7CxZiK1_Qtp2L3k0c55rm6jNRlWfxzpt4367fVk96-PD6vllsdHTqja3bGtCU1FYOYloXB1L5sAjXMJACGyUP0VWRDEiE4tm200ROHalpxpm7_uokG6fec6Hxu7ypXI_4CkMVDfA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>book</recordtype></control><display><type>book</type><title>Ensemble Forecasting Applied to Power Systems</title><source>DOAB: Directory of Open Access Books</source><creator>Bracale, Antonio ; Falco, Pasquale De</creator><creatorcontrib>Bracale, Antonio ; Falco, Pasquale De</creatorcontrib><description>Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.</description><identifier>ISBN: 3039283138</identifier><identifier>ISBN: 303928312X</identifier><identifier>ISBN: 9783039283132</identifier><identifier>ISBN: 9783039283125</identifier><identifier>DOI: 10.3390/books978-3-03928-313-2</identifier><language>eng</language><publisher>MDPI - Multidisciplinary Digital Publishing Institute</publisher><subject>autoregression ; average probability forecast ; calibration window ; clear sky index ; clearness index ; combining forecasts ; conditional predictive ability ; deep learning ; distributed energy resources ; distributed generation ; electric load forecasting ; electricity price forecasting ; energy management ; ensemble methods ; extreme learning machine ; forecast combination ; forecasting ; Fourier series ; heuristic algorithm ; hierarchical load forecasting ; History of engineering and technology ; interval prediction ; kernel density estimation ; lower and upper bound estimation ; microgrid ; photovoltaic power ; pinball score ; predictive distribution ; probabilistic forecasting ; smart grids ; solar energy ; solar farm ; solar power prediction ; solar PV ; T1-995 ; TA1-2040 ; Technology, Engineering, Agriculture, Industrial processes ; Technology: general issues ; weather station combination</subject><creationdate>2020</creationdate><tpages>134</tpages><format>134</format><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>306,776,780,782,27902,55285</link.rule.ids></links><search><creatorcontrib>Bracale, Antonio</creatorcontrib><creatorcontrib>Falco, Pasquale De</creatorcontrib><title>Ensemble Forecasting Applied to Power Systems</title><description>Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.</description><subject>autoregression</subject><subject>average probability forecast</subject><subject>calibration window</subject><subject>clear sky index</subject><subject>clearness index</subject><subject>combining forecasts</subject><subject>conditional predictive ability</subject><subject>deep learning</subject><subject>distributed energy resources</subject><subject>distributed generation</subject><subject>electric load forecasting</subject><subject>electricity price forecasting</subject><subject>energy management</subject><subject>ensemble methods</subject><subject>extreme learning machine</subject><subject>forecast combination</subject><subject>forecasting</subject><subject>Fourier series</subject><subject>heuristic algorithm</subject><subject>hierarchical load forecasting</subject><subject>History of engineering and technology</subject><subject>interval prediction</subject><subject>kernel density estimation</subject><subject>lower and upper bound estimation</subject><subject>microgrid</subject><subject>photovoltaic power</subject><subject>pinball score</subject><subject>predictive distribution</subject><subject>probabilistic forecasting</subject><subject>smart grids</subject><subject>solar energy</subject><subject>solar farm</subject><subject>solar power prediction</subject><subject>solar PV</subject><subject>T1-995</subject><subject>TA1-2040</subject><subject>Technology, Engineering, Agriculture, Industrial processes</subject><subject>Technology: general issues</subject><subject>weather station combination</subject><isbn>3039283138</isbn><isbn>303928312X</isbn><isbn>9783039283132</isbn><isbn>9783039283125</isbn><fulltext>true</fulltext><rsrctype>book</rsrctype><creationdate>2020</creationdate><recordtype>book</recordtype><sourceid>V1H</sourceid><recordid>eNotj91KAzEQRgMiqG2fQJB9gegkk_3JZSmtCgWF6nWZ7Eykut0szYL49i7Vq8PhgwOfUncG7hE9PISUvrKvG40a0NuJBrW9UDd41smaK7XI-RMArIe6rPBa6XWf5Rg6KTbpJC3l8dB_FMth6A7CxZiK1_Qtp2L3k0c55rm6jNRlWfxzpt4367fVk96-PD6vllsdHTqja3bGtCU1FYOYloXB1L5sAjXMJACGyUP0VWRDEiE4tm200ROHalpxpm7_uokG6fec6Hxu7ypXI_4CkMVDfA</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Bracale, Antonio</creator><creator>Falco, Pasquale De</creator><general>MDPI - Multidisciplinary Digital Publishing Institute</general><scope>V1H</scope></search><sort><creationdate>2020</creationdate><title>Ensemble Forecasting Applied to Power Systems</title><author>Bracale, Antonio ; Falco, Pasquale De</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-f4341-7d411c5a86d0e1cded017958ba8ddae001da90f96fd1aef0b4d2cf2f9adb60013</frbrgroupid><rsrctype>books</rsrctype><prefilter>books</prefilter><language>eng</language><creationdate>2020</creationdate><topic>autoregression</topic><topic>average probability forecast</topic><topic>calibration window</topic><topic>clear sky index</topic><topic>clearness index</topic><topic>combining forecasts</topic><topic>conditional predictive ability</topic><topic>deep learning</topic><topic>distributed energy resources</topic><topic>distributed generation</topic><topic>electric load forecasting</topic><topic>electricity price forecasting</topic><topic>energy management</topic><topic>ensemble methods</topic><topic>extreme learning machine</topic><topic>forecast combination</topic><topic>forecasting</topic><topic>Fourier series</topic><topic>heuristic algorithm</topic><topic>hierarchical load forecasting</topic><topic>History of engineering and technology</topic><topic>interval prediction</topic><topic>kernel density estimation</topic><topic>lower and upper bound estimation</topic><topic>microgrid</topic><topic>photovoltaic power</topic><topic>pinball score</topic><topic>predictive distribution</topic><topic>probabilistic forecasting</topic><topic>smart grids</topic><topic>solar energy</topic><topic>solar farm</topic><topic>solar power prediction</topic><topic>solar PV</topic><topic>T1-995</topic><topic>TA1-2040</topic><topic>Technology, Engineering, Agriculture, Industrial processes</topic><topic>Technology: general issues</topic><topic>weather station combination</topic><toplevel>online_resources</toplevel><creatorcontrib>Bracale, Antonio</creatorcontrib><creatorcontrib>Falco, Pasquale De</creatorcontrib><collection>DOAB: Directory of Open Access Books</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bracale, Antonio</au><au>Falco, Pasquale De</au><format>book</format><genre>book</genre><ristype>BOOK</ristype><btitle>Ensemble Forecasting Applied to Power Systems</btitle><date>2020</date><risdate>2020</risdate><isbn>3039283138</isbn><isbn>303928312X</isbn><isbn>9783039283132</isbn><isbn>9783039283125</isbn><abstract>Modern power systems are affected by many sources of uncertainty, driven by the spread of renewable generation, by the development of liberalized energy market systems and by the intrinsic random behavior of the final energy customers. Forecasting is, therefore, a crucial task in planning and managing modern power systems at any level: from transmission to distribution networks, and in also the new context of smart grids. Recent trends suggest the suitability of ensemble approaches in order to increase the versatility and robustness of forecasting systems. Stacking, boosting, and bagging techniques have recently started to attract the interest of power system practitioners. This book addresses the development of new, advanced, ensemble forecasting methods applied to power systems, collecting recent contributions to the development of accurate forecasts of energy-related variables by some of the most qualified experts in energy forecasting. Typical areas of research (renewable energy forecasting, load forecasting, energy price forecasting) are investigated, with relevant applications to the use of forecasts in energy management systems.</abstract><pub>MDPI - Multidisciplinary Digital Publishing Institute</pub><doi>10.3390/books978-3-03928-313-2</doi><tpages>134</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISBN: 3039283138
ispartof
issn
language eng
recordid cdi_oapen_doabooks_46473
source DOAB: Directory of Open Access Books
subjects autoregression
average probability forecast
calibration window
clear sky index
clearness index
combining forecasts
conditional predictive ability
deep learning
distributed energy resources
distributed generation
electric load forecasting
electricity price forecasting
energy management
ensemble methods
extreme learning machine
forecast combination
forecasting
Fourier series
heuristic algorithm
hierarchical load forecasting
History of engineering and technology
interval prediction
kernel density estimation
lower and upper bound estimation
microgrid
photovoltaic power
pinball score
predictive distribution
probabilistic forecasting
smart grids
solar energy
solar farm
solar power prediction
solar PV
T1-995
TA1-2040
Technology, Engineering, Agriculture, Industrial processes
Technology: general issues
weather station combination
title Ensemble Forecasting Applied to Power Systems
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T18%3A09%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-oapen&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=book&rft.btitle=Ensemble%20Forecasting%20Applied%20to%20Power%20Systems&rft.au=Bracale,%20Antonio&rft.date=2020&rft.isbn=3039283138&rft.isbn_list=303928312X&rft.isbn_list=9783039283132&rft.isbn_list=9783039283125&rft_id=info:doi/10.3390/books978-3-03928-313-2&rft_dat=%3Coapen%3E46473%3C/oapen%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true