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...
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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 |
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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. 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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 |
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