Dynamic feedback neuro-evolutionary networks for forecasting the highly fluctuating electrical loads

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@Article{Khan:2016:GPEM,

• author = "Gul Muhammad Khan and Faheem Zafari",
• title = "Dynamic feedback neuro-evolutionary networks for forecasting the highly fluctuating electrical loads",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2016",
• volume = "17",
• number = "4",
• pages = "391--408",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Very short term electric load forecasting (VSTLF), Recurrent neural networks, Cartesian genetic programming evolved recurrent neural network (CGPRNN), Neuro-evolution",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-016-9268-6",
• size = "18 pages",
• abstract = "A computationally efficient and accurate forecasting model for highly dynamic electric load patterns of UK electric power grid is proposed and implemented using recurrent neuro-evolutionary algorithms. Cartesian genetic programming is used to find the optimum recurrent structure and network parameters to accurately forecast highly fluctuating load patterns. Fifty different models are trained and tested in diverse set of scenarios to predict single as well as more future instances in advance. The testing results demonstrated that the models are highly accurate as they attained an accuracy of as high as 98.95 percent. The models trained to predict single future instances are tested to predict more future instances in advance, obtaining an accuracy of 94 percent, thus proving their robustness to predict any time series.",

}

Genetic Programming entries for Gul Muhammad Khan Faheem Zafari

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