dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-12T01:00:10Z
dc.date.accessioned2022-12-19T20:37:27Z
dc.date.available2020-12-12T01:00:10Z
dc.date.available2022-12-19T20:37:27Z
dc.date.created2020-12-12T01:00:10Z
dc.date.issued2020-02-01
dc.identifierElectric Power Systems Research, v. 179.
dc.identifier0378-7796
dc.identifierhttp://hdl.handle.net/11449/198136
dc.identifier10.1016/j.epsr.2019.106096
dc.identifier2-s2.0-85074928091
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5378770
dc.description.abstractA multinodal intelligent predictive method for electrical power systems has been developed. Knowing the electrical load accurately and in advance is essential for conducting studies in regard to the system operations, and to create strategies that improve the quality of the energy-supply for commercial, industrial, and residential consumers. The proposed method employs a supervised Fuzzy-ARTMAP neural network, using the new concept of reverse training, to forecast the global demand and load of several nodes of an electric network (multinodal load forecasting) up to 24 h ahead. To evaluate and test the proposed system, an application is presented that considers real historical data from a company in the electric sector. Results show that the reverse training reduces the error of the neural network, making the forecast more accurate, reliable, and very fast.
dc.languageeng
dc.relationElectric Power Systems Research
dc.sourceScopus
dc.subjectAdaptive resonance theory
dc.subjectArtificial neural networks
dc.subjectElectrical power systems
dc.subjectMultinodal load forecasting
dc.titleA new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
dc.typeArtículos de revistas


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