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• W2087782660 abstract "In this paper, a new hybrid method based on DWT and WNN methods is proposed to develop an accurate model for short term load forecasting. The wavelet filters are used to decompose the load series into deterministic and fluctuation components. The deterministic component models the general pattern of the load signal while the fluctuation component models the random characteristics of the load signal. The WNN technique is then used to find and weight similar data to predict the next day load. The effectiveness of the proposed methodology in forecasting the next day load is illustrated by applying it to practical load data of California and Spain energy markets. The performance is compared to the corresponding non-wavelet model and Naive predictors by using the weekly mean absolute percentage error metric and the weekly error variance." @default.
• W2087782660 created "2016-06-24" @default.
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• W2087782660 date "2014-09-01" @default.
• W2087782660 modified "2023-09-26" @default.
• W2087782660 title "A methodology for short term load forecasting based on wavelet filters" @default.
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• W2087782660 doi "https://doi.org/10.1142/s2335680414500124" @default.
• W2087782660 hasPublicationYear "2014" @default.
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