{"id":601,"date":"2016-07-18T17:56:44","date_gmt":"2016-07-18T20:56:44","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=601"},"modified":"2016-07-18T17:56:44","modified_gmt":"2016-07-18T20:56:44","slug":"vol10-no2-art6","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/","title":{"rendered":"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Monthly Electric Energy Demand Forecasting By Fuzzy Inference System<\/p>\n<p><strong>Autores:<\/strong> Luna, Ivette; Ballini, Rosangela<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> This paper presents the use of a fuzzy rule-based system for mid-term electric energy demand forecasting. The results are achieved for an specific region at the southeastern part of Brazil. The total demand is divided in three groups of consuption: residential, industrial and commercial. The forecasting model adopted is based on Takagi-Sugeno fuzzy rules, where the number of fuzzy rules is defined by the Subtractive Clustering algorithm, an unsupervised approach applied over an in-sample data set. A fuzzy rule base is determined by each group of consumption and the model parameters are adjusted using the Expectation-Maximization optimization algorithm. As input variables are considered the observations of demand in  previous moments as well as macroeconomic explanatory variables. Forecasting tests over in-sample and out-of-sample data sets are developed. The results show the adequacy of the models adjusted, achieving annual absolute percentage errors of 3% in average.<\/p>\n<p><strong>Palavras-chave:<\/strong> Electric energy demand; fuzzy inference system; forecasting; time series<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 8<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol10-no2-art6\">10.21528\/lmln-vol10-no2-art6<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no2-art6.pdf\" rel=\"\">vol10-no2-art6.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no2-art6.bib\" rel=\"\">vol10-no2-art6.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Monthly Electric Energy Demand Forecasting By Fuzzy Inference System Autores: Luna, Ivette; Ballini, Rosangela Resumo: This paper presents the use of a fuzzy rule-based system for mid-term electric energy demand forecasting. The results are achieved for an specific region <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":585,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-601","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Monthly Electric Energy Demand Forecasting By Fuzzy Inference System - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Monthly Electric Energy Demand Forecasting By Fuzzy Inference System Autores: Luna, Ivette; Ballini, Rosangela Resumo: This paper presents the use of a fuzzy rule-based system for mid-term electric energy demand forecasting. The results are achieved for an specific region Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/\",\"name\":\"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"datePublished\":\"2016-07-18T20:56:44+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 10 &#8211; N\u00famero 2\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/","og_locale":"pt_BR","og_type":"article","og_title":"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System - Learning and NonLinear Models","og_description":"T\u00edtulo: Monthly Electric Energy Demand Forecasting By Fuzzy Inference System Autores: Luna, Ivette; Ballini, Rosangela Resumo: This paper presents the use of a fuzzy rule-based system for mid-term electric energy demand forecasting. The results are achieved for an specific region Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/","og_site_name":"Learning and NonLinear Models","twitter_card":"summary_large_image","twitter_misc":{"Est. tempo de leitura":"1 minuto"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/","name":"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"datePublished":"2016-07-18T20:56:44+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art6\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Browse issues","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/"},{"@type":"ListItem","position":2,"name":"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 10 &#8211; N\u00famero 2","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/"},{"@type":"ListItem","position":3,"name":"Monthly Electric Energy Demand Forecasting By Fuzzy Inference System"}]},{"@type":"WebSite","@id":"https:\/\/sbia.org.br\/lnlm\/#website","url":"https:\/\/sbia.org.br\/lnlm\/","name":"Learning and NonLinear Models","description":"","publisher":{"@id":"https:\/\/sbia.org.br\/lnlm\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/sbia.org.br\/lnlm\/#organization","name":"Learning and NonLinear Models","url":"https:\/\/sbia.org.br\/lnlm\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","width":398,"height":94,"caption":"Learning and NonLinear Models"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/601","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/comments?post=601"}],"version-history":[{"count":0,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/601\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/585"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=601"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}