Electricity Load Forecasting for Distribution Network Based on Long Short-term Memory Recurrent Neural Network

Liu, L.F.; Lang, Junwei; Yue, Qing; Dong, Haiqing; Sun, Kai; Zhang, L.L.

doi:10.1049/cp.2018.1798

Cited by 2 publications

(1 citation statement)

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“…The method is easy to use and includes the advantages of regression analysis and Fourier analysis that makes it relevant for forecasting electricity consumption. Earlier, the SSA method was used only for linear data series [16][17][18]. The result of applying the SSA method is the analysis and identification of anomalous values (outliers) from the initial data series and a decrease of their influence on the forecast quality, as well as the determination of systematic components (trends).…”

Section: Waveletmentioning

confidence: 99%

Analysis of methods of electricity consumption forecasting for a coal industry enterprise

Manusov

Orlov

Karmanov

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The paper considers a forecast model of electricity consumption of a coal industry enterprise based on three forecast methods, namely the wavelet transform, the vector method, and the recurrent neutral network. For preprocessing the data for forecasting by vector and recurrent methods, the Singular Spectrum Analysis method was chosen. The structure of the model allows taking into account individual features of the operating cycle of the production process and smoothing the noise components and outliers caused by these features. The results of a short-term hourly forecast for one day ahead are presented with the comparison of the obtained values. The results of short-term electricity consumption forecast were verified based on the actual data of the coal industry enterprise in order to assess the adequacy of the model to the actual values.

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Section: Waveletmentioning

confidence: 99%