Application of neural networks for short-term load forecasting
This paper presents the development of an artificial neural network based short-term load forecasting model. The model can forecast daily load profiles with a load time of one day for next 24 hours. In this method can divide days of year with using average temperature. Groups make according linearit...
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| creator | Afkhami, R. Yazdi, F.M. |
| description | This paper presents the development of an artificial neural network based short-term load forecasting model. The model can forecast daily load profiles with a load time of one day for next 24 hours. In this method can divide days of year with using average temperature. Groups make according linearity rate of curve. Ultimate forecast for each group obtain with considering weekday and weekend. 24 hours of a day divided to 3 groups at 8 hours, network for every each of eight varieties must interpolate. This paper investigates effects of temperature and humidity on consuming curve. For forecasting load curve of holidays at first calculate pick and valley and then the neural network forecast is re-shaped with the new data. The networks are trained using hourly historical load data and daily historical max/min temperature and humidity data. The results of testing the system on data from yazd utility are reported |
| doi_str_mv | 10.1109/POWERI.2006.1632536 |
| format | Conference Proceeding |
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The results of testing the system on data from yazd utility are reported</description><identifier>ISBN: 0780395255</identifier><identifier>ISBN: 9780780395251</identifier><identifier>DOI: 10.1109/POWERI.2006.1632536</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Economic forecasting ; Load forecasting ; Load modeling ; Neural networks ; Neurons ; Predictive models ; Temperature ; Training data ; Weather forecasting</subject><ispartof>2006 IEEE Power India Conference, 2006, p.5 pp.</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c140t-33a3be452a4da033ba8f80de889fb9847d09770b5e9d1d6831538cd12a8ccefe3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1632536$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1632536$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Afkhami, R.</creatorcontrib><creatorcontrib>Yazdi, F.M.</creatorcontrib><title>Application of neural networks for short-term load forecasting</title><title>2006 IEEE Power India Conference</title><addtitle>POWERI</addtitle><description>This paper presents the development of an artificial neural network based short-term load forecasting model. The model can forecast daily load profiles with a load time of one day for next 24 hours. In this method can divide days of year with using average temperature. Groups make according linearity rate of curve. Ultimate forecast for each group obtain with considering weekday and weekend. 24 hours of a day divided to 3 groups at 8 hours, network for every each of eight varieties must interpolate. This paper investigates effects of temperature and humidity on consuming curve. For forecasting load curve of holidays at first calculate pick and valley and then the neural network forecast is re-shaped with the new data. The networks are trained using hourly historical load data and daily historical max/min temperature and humidity data. The results of testing the system on data from yazd utility are reported</description><subject>Artificial neural networks</subject><subject>Economic forecasting</subject><subject>Load forecasting</subject><subject>Load modeling</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Predictive models</subject><subject>Temperature</subject><subject>Training data</subject><subject>Weather forecasting</subject><isbn>0780395255</isbn><isbn>9780780395251</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj1FLwzAUhQMiqHO_YC_9A603vU2bvAhjTB0MJmPi40iTG612TUki4r-34s7Lx_keDhzGFhwKzkHdPe9e1_tNUQLUBa-xFFhfsBtoJKASpRBXbB7jB0yZeqPENbtfjmPfGZ06P2TeZQN9Bd1PSN8-fMbM-ZDFdx9Sniicst5r--fI6Ji64e2WXTrdR5qfOWMvD-vD6inf7h43q-U2N7yClCNqbKkSpa6sBsRWSyfBkpTKtUpWjQXVNNAKUpbbWiIXKI3lpZbGkCOcscX_bkdExzF0Jx1-jueL-As5q0hZ</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Afkhami, R.</creator><creator>Yazdi, F.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2006</creationdate><title>Application of neural networks for short-term load forecasting</title><author>Afkhami, R. ; Yazdi, F.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c140t-33a3be452a4da033ba8f80de889fb9847d09770b5e9d1d6831538cd12a8ccefe3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Artificial neural networks</topic><topic>Economic forecasting</topic><topic>Load forecasting</topic><topic>Load modeling</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Predictive models</topic><topic>Temperature</topic><topic>Training data</topic><topic>Weather forecasting</topic><toplevel>online_resources</toplevel><creatorcontrib>Afkhami, R.</creatorcontrib><creatorcontrib>Yazdi, F.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Afkhami, R.</au><au>Yazdi, F.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Application of neural networks for short-term load forecasting</atitle><btitle>2006 IEEE Power India Conference</btitle><stitle>POWERI</stitle><date>2006</date><risdate>2006</risdate><spage>5 pp.</spage><pages>5 pp.-</pages><isbn>0780395255</isbn><isbn>9780780395251</isbn><abstract>This paper presents the development of an artificial neural network based short-term load forecasting model. The model can forecast daily load profiles with a load time of one day for next 24 hours. In this method can divide days of year with using average temperature. Groups make according linearity rate of curve. Ultimate forecast for each group obtain with considering weekday and weekend. 24 hours of a day divided to 3 groups at 8 hours, network for every each of eight varieties must interpolate. This paper investigates effects of temperature and humidity on consuming curve. For forecasting load curve of holidays at first calculate pick and valley and then the neural network forecast is re-shaped with the new data. The networks are trained using hourly historical load data and daily historical max/min temperature and humidity data. The results of testing the system on data from yazd utility are reported</abstract><pub>IEEE</pub><doi>10.1109/POWERI.2006.1632536</doi></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Economic forecasting Load forecasting Load modeling Neural networks Neurons Predictive models Temperature Training data Weather forecasting |
| title | Application of neural networks for short-term load forecasting |
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