Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting

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dc.contributor.authorÇetinkaya, Nurettin
dc.date.accessioned2020-03-26T19:32:34Z
dc.date.available2020-03-26T19:32:34Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractLoad forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is inneed to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problemsin literature. In addition to numerical inputs, ANFIShas linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error(MAE) are used. The obtained results show that the proposedalgorithms are available.en_US
dc.identifier.citationÇetinkaya, N., (2017). Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 168-173.
dc.identifier.endpage173en_US
dc.identifier.issn2147-6799en_US
dc.identifier.issue4en_US
dc.identifier.startpage168en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TWpjeE9EWTNOdz09
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34494
dc.identifier.volume5en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems and Applications in Engineeringen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYapay Zekaen_US
dc.subjectANFIS
dc.subjectDynamic programming
dc.subjectElectrical load forecasting
dc.subjectENPEP
dc.subjectMAED
dc.titleImproving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecastingen_US
dc.typeArticleen_US

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