Forecasts of PV power output using power measurements of 80 residential PV installs
We describe a new method to forecast the power output from photovoltaic (PV) systems under cloudy skies. We forecast cloud-induced fluctuations in output power by using measurements from 80 residential rooftop PV systems as an input to a forecasting algorithm described here. We compare the performan...
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| creator | Lonij, V. P. A. Jayadevan, V. T. Brooks, A. E. Rodriguez, J. J. Koch, K. Leuthold, M. Cronin, A. D. |
| description | We describe a new method to forecast the power output from photovoltaic (PV) systems under cloudy skies. We forecast cloud-induced fluctuations in output power by using measurements from 80 residential rooftop PV systems as an input to a forecasting algorithm described here. We compare the performance of our new method to results from our numerical weather model, and also to forecasts based on our images from a ground-based sun-tracking sky camera. Our numerical weather model provides forecasts of irradiance up to several days in advance. In comparison, our network of PV systems can forecast output up to an hour in advance. Our sky-camera image analysis algorithms can be used to forecast 10 minutes in advance. We also show how hybrid methods can improve the accuracy of hour-ahead forecasts. |
| doi_str_mv | 10.1109/PVSC.2012.6318280 |
| format | Conference Proceeding |
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P. A. ; Jayadevan, V. T. ; Brooks, A. E. ; Rodriguez, J. J. ; Koch, K. ; Leuthold, M. ; Cronin, A. D.</creator><creatorcontrib>Lonij, V. P. A. ; Jayadevan, V. T. ; Brooks, A. E. ; Rodriguez, J. J. ; Koch, K. ; Leuthold, M. ; Cronin, A. D.</creatorcontrib><description>We describe a new method to forecast the power output from photovoltaic (PV) systems under cloudy skies. We forecast cloud-induced fluctuations in output power by using measurements from 80 residential rooftop PV systems as an input to a forecasting algorithm described here. We compare the performance of our new method to results from our numerical weather model, and also to forecasts based on our images from a ground-based sun-tracking sky camera. Our numerical weather model provides forecasts of irradiance up to several days in advance. In comparison, our network of PV systems can forecast output up to an hour in advance. Our sky-camera image analysis algorithms can be used to forecast 10 minutes in advance. 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P. A.</creatorcontrib><creatorcontrib>Jayadevan, V. T.</creatorcontrib><creatorcontrib>Brooks, A. E.</creatorcontrib><creatorcontrib>Rodriguez, J. J.</creatorcontrib><creatorcontrib>Koch, K.</creatorcontrib><creatorcontrib>Leuthold, M.</creatorcontrib><creatorcontrib>Cronin, A. D.</creatorcontrib><title>Forecasts of PV power output using power measurements of 80 residential PV installs</title><title>2012 38th IEEE Photovoltaic Specialists Conference</title><addtitle>PVSC</addtitle><description>We describe a new method to forecast the power output from photovoltaic (PV) systems under cloudy skies. We forecast cloud-induced fluctuations in output power by using measurements from 80 residential rooftop PV systems as an input to a forecasting algorithm described here. We compare the performance of our new method to results from our numerical weather model, and also to forecasts based on our images from a ground-based sun-tracking sky camera. Our numerical weather model provides forecasts of irradiance up to several days in advance. In comparison, our network of PV systems can forecast output up to an hour in advance. Our sky-camera image analysis algorithms can be used to forecast 10 minutes in advance. We also show how hybrid methods can improve the accuracy of hour-ahead forecasts.</description><subject>Clouds</subject><subject>Forecasting</subject><subject>Numerical models</subject><subject>Photovoltaic systems</subject><subject>Power measurement</subject><subject>Predictive models</subject><subject>Smart grids</subject><subject>Solar energy</subject><subject>Solar power generation</subject><subject>Weather forecasting</subject><issn>0160-8371</issn><isbn>1467300640</isbn><isbn>9781467300643</isbn><isbn>9781467300667</isbn><isbn>1467300667</isbn><isbn>1467300659</isbn><isbn>9781467300650</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kN1Kw0AUhFdUsK19APEmL5B4zu5ms3spwVahYKGlt2WTnJWV_JFNEN9epfFq-IaZuRjGHhASRDBP-9MhTzggT5RAzTVcsbXJNEqVCQClsmu2_AcJN2wBqCDWIsM7tgzhE4CDULhgh003UGnDGKLORftT1HdfNETdNPbTGE3Btx-z1ZAN00ANtZeshmig4Ktf9rb-q_o2jLauwz27dbYOtJ51xY6bl2P-Gu_et2_58y72BsZYU6q4sWRUyUlmyqExUBSVKkDaVEteCiFNwau0LJxDSYVyGTor0AkLlRMr9niZ9UR07gff2OH7PP8hfgCuvlK-</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Lonij, V. P. A.</creator><creator>Jayadevan, V. T.</creator><creator>Brooks, A. E.</creator><creator>Rodriguez, J. J.</creator><creator>Koch, K.</creator><creator>Leuthold, M.</creator><creator>Cronin, A. D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201206</creationdate><title>Forecasts of PV power output using power measurements of 80 residential PV installs</title><author>Lonij, V. P. A. ; Jayadevan, V. T. ; Brooks, A. E. ; Rodriguez, J. J. ; Koch, K. ; Leuthold, M. ; Cronin, A. 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D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lonij, V. P. A.</au><au>Jayadevan, V. T.</au><au>Brooks, A. E.</au><au>Rodriguez, J. J.</au><au>Koch, K.</au><au>Leuthold, M.</au><au>Cronin, A. D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Forecasts of PV power output using power measurements of 80 residential PV installs</atitle><btitle>2012 38th IEEE Photovoltaic Specialists Conference</btitle><stitle>PVSC</stitle><date>2012-06</date><risdate>2012</risdate><spage>003300</spage><epage>003305</epage><pages>003300-003305</pages><issn>0160-8371</issn><isbn>1467300640</isbn><isbn>9781467300643</isbn><eisbn>9781467300667</eisbn><eisbn>1467300667</eisbn><eisbn>1467300659</eisbn><eisbn>9781467300650</eisbn><abstract>We describe a new method to forecast the power output from photovoltaic (PV) systems under cloudy skies. We forecast cloud-induced fluctuations in output power by using measurements from 80 residential rooftop PV systems as an input to a forecasting algorithm described here. We compare the performance of our new method to results from our numerical weather model, and also to forecasts based on our images from a ground-based sun-tracking sky camera. Our numerical weather model provides forecasts of irradiance up to several days in advance. In comparison, our network of PV systems can forecast output up to an hour in advance. Our sky-camera image analysis algorithms can be used to forecast 10 minutes in advance. We also show how hybrid methods can improve the accuracy of hour-ahead forecasts.</abstract><pub>IEEE</pub><doi>10.1109/PVSC.2012.6318280</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 0160-8371 |
| ispartof | 2012 38th IEEE Photovoltaic Specialists Conference, 2012, p.003300-003305 |
| issn | 0160-8371 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Clouds Forecasting Numerical models Photovoltaic systems Power measurement Predictive models Smart grids Solar energy Solar power generation Weather forecasting |
| title | Forecasts of PV power output using power measurements of 80 residential PV installs |
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