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Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs
The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid perf...
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Published in: | IEEE transactions on industrial informatics 2021-03, Vol.17 (3), p.1873-1881 |
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container_title | IEEE transactions on industrial informatics |
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creator | Abdel-Nasser, Mohamed Mahmoud, Karar Lehtonen, Matti |
description | The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles. |
doi_str_mv | 10.1109/TII.2020.2996235 |
format | article |
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A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. 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A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. 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(IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9979-7333</orcidid><orcidid>https://orcid.org/0000-0002-6729-6809</orcidid><orcidid>https://orcid.org/0000-0002-1074-2441</orcidid></search><sort><creationdate>20210301</creationdate><title>Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs</title><author>Abdel-Nasser, Mohamed ; Mahmoud, Karar ; Lehtonen, Matti</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-40273131998ceeddb539dfae06d1772ca7061378ab8e80836a4cfa880b72fada3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Agglomeration</topic><topic>Choquet integral</topic><topic>deep long short-term memory (LSTM)</topic><topic>Forecasting</topic><topic>Fuzzy set theory</topic><topic>Integrals</topic><topic>Irradiance</topic><topic>irradiance forecasting</topic><topic>Long short term memory</topic><topic>Mathematical models</topic><topic>photovoltaic (PV)</topic><topic>Photovoltaic cells</topic><topic>Photovoltaic systems</topic><topic>Power generation planning</topic><topic>Power generation reliability</topic><topic>Reliability</topic><topic>Smart grid</topic><topic>Solar power generation</topic><toplevel>online_resources</toplevel><creatorcontrib>Abdel-Nasser, Mohamed</creatorcontrib><creatorcontrib>Mahmoud, Karar</creatorcontrib><creatorcontrib>Lehtonen, Matti</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEL</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on industrial informatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abdel-Nasser, Mohamed</au><au>Mahmoud, Karar</au><au>Lehtonen, Matti</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs</atitle><jtitle>IEEE transactions on industrial informatics</jtitle><stitle>TII</stitle><date>2021-03-01</date><risdate>2021</risdate><volume>17</volume><issue>3</issue><spage>1873</spage><epage>1881</epage><pages>1873-1881</pages><issn>1551-3203</issn><eissn>1941-0050</eissn><coden>ITIICH</coden><abstract>The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. 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subjects | Agglomeration Choquet integral deep long short-term memory (LSTM) Forecasting Fuzzy set theory Integrals Irradiance irradiance forecasting Long short term memory Mathematical models photovoltaic (PV) Photovoltaic cells Photovoltaic systems Power generation planning Power generation reliability Reliability Smart grid Solar power generation |
title | Reliable Solar Irradiance Forecasting Approach Based on Choquet Integral and Deep LSTMs |
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