Short-Term Load Forecasting 2019
Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these...
Saved in:
HerausgeberIn: | |
---|---|
Sonstige: | |
Year of Publication: | 2021 |
Language: | English |
Physical Description: | 1 electronic resource (324 p.) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
993545743004498 |
---|---|
ctrlnum |
(CKB)5400000000042888 (oapen)https://directory.doabooks.org/handle/20.500.12854/68414 (EXLCZ)995400000000042888 |
collection |
bib_alma |
record_format |
marc |
spelling |
Gabaldón, Antonio edt Short-Term Load Forecasting 2019 Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 1 electronic resource (324 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system. English History of engineering & technology bicssc short-term load forecasting demand-side management pattern similarity hierarchical short-term load forecasting feature selection weather station selection load forecasting special days regressive models electric load forecasting data preprocessing technique multiobjective optimization algorithm combined model Nordic electricity market electricity demand component estimation method univariate and multivariate time series analysis modeling and forecasting deep learning wavenet long short-term memory demand response hybrid energy system data augmentation convolution neural network residential load forecasting forecasting time series cubic splines real-time electricity load seasonal patterns Load forecasting VSTLF bus load forecasting DBN PSR distributed energy resources prosumers building electric energy consumption forecasting cold-start problem transfer learning multivariate random forests random forest electricity consumption lasso Tikhonov regularization load metering preliminary load short term load forecasting performance criteria power systems cost analysis day ahead feature extraction deep residual neural network multiple sources electricity 3-03943-442-X 3-03943-443-8 Ruiz-Abellón, Dr. María Carmen edt Fernández-Jiménez, Luis Alfredo edt Gabaldón, Antonio oth Ruiz-Abellón, Dr. María Carmen oth Fernández-Jiménez, Luis Alfredo oth |
language |
English |
format |
eBook |
author2 |
Ruiz-Abellón, Dr. María Carmen Fernández-Jiménez, Luis Alfredo Gabaldón, Antonio Ruiz-Abellón, Dr. María Carmen Fernández-Jiménez, Luis Alfredo |
author_facet |
Ruiz-Abellón, Dr. María Carmen Fernández-Jiménez, Luis Alfredo Gabaldón, Antonio Ruiz-Abellón, Dr. María Carmen Fernández-Jiménez, Luis Alfredo |
author2_variant |
a g ag d m c r a dmcr dmcra l a f j laf lafj |
author2_role |
HerausgeberIn HerausgeberIn Sonstige Sonstige Sonstige |
title |
Short-Term Load Forecasting 2019 |
spellingShingle |
Short-Term Load Forecasting 2019 |
title_full |
Short-Term Load Forecasting 2019 |
title_fullStr |
Short-Term Load Forecasting 2019 |
title_full_unstemmed |
Short-Term Load Forecasting 2019 |
title_auth |
Short-Term Load Forecasting 2019 |
title_new |
Short-Term Load Forecasting 2019 |
title_sort |
short-term load forecasting 2019 |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
physical |
1 electronic resource (324 p.) |
isbn |
3-03943-442-X 3-03943-443-8 |
illustrated |
Not Illustrated |
work_keys_str_mv |
AT gabaldonantonio shorttermloadforecasting2019 AT ruizabellondrmariacarmen shorttermloadforecasting2019 AT fernandezjimenezluisalfredo shorttermloadforecasting2019 |
status_str |
n |
ids_txt_mv |
(CKB)5400000000042888 (oapen)https://directory.doabooks.org/handle/20.500.12854/68414 (EXLCZ)995400000000042888 |
carrierType_str_mv |
cr |
is_hierarchy_title |
Short-Term Load Forecasting 2019 |
author2_original_writing_str_mv |
noLinkedField noLinkedField noLinkedField noLinkedField noLinkedField |
_version_ |
1796648786792022016 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04727nam-a2201021z--4500</leader><controlfield tag="001">993545743004498</controlfield><controlfield tag="005">20231214133528.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202105s2021 xx |||||o ||| 0|eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)5400000000042888</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/68414</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)995400000000042888</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gabaldón, Antonio</subfield><subfield code="4">edt</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Short-Term Load Forecasting 2019</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Basel, Switzerland</subfield><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (324 p.)</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">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">History of engineering & technology</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">short-term load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">demand-side management</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">pattern similarity</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hierarchical short-term load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature selection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">weather station selection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">special days</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">regressive models</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">electric load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">data preprocessing technique</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multiobjective optimization algorithm</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">combined model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Nordic electricity market</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">electricity demand</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">component estimation method</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">univariate and multivariate time series analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">modeling and forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">deep learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">wavenet</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">long short-term memory</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">demand response</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hybrid energy system</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">data augmentation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">convolution neural network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">residential load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">time series</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cubic splines</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">real-time electricity load</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">seasonal patterns</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">VSTLF</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">bus load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">DBN</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">PSR</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">distributed energy resources</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">prosumers</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">building electric energy consumption forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cold-start problem</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">transfer learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multivariate random forests</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">random forest</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">electricity consumption</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">lasso</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Tikhonov regularization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">load metering</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">preliminary load</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">short term load forecasting</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">performance criteria</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">power systems</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">cost analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">day ahead</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature extraction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">deep residual neural network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multiple sources</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">electricity</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03943-442-X</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03943-443-8</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ruiz-Abellón, Dr. María Carmen</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fernández-Jiménez, Luis Alfredo</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gabaldón, Antonio</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ruiz-Abellón, Dr. María Carmen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fernández-Jiménez, Luis Alfredo</subfield><subfield code="4">oth</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-12-15 05:56:46 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2022-04-04 09:22:53 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338055490004498&Force_direct=true</subfield><subfield code="Z">5338055490004498</subfield><subfield code="b">Available</subfield><subfield code="8">5338055490004498</subfield></datafield></record></collection> |