Analysis of electricity consumption forecasting methods for the coal industry

• Autorzy: Manusov Vadim Zinovievich , Orlov Dmitry , Karmanov Vitaly Sergeevich , Khusnutdinov Alexander Olegovich , Kokin Sergey Evgenevich , Safaraliev Murodbek

Identyfikatory

DOI

10.15199/48.2022.09.05

Warianty tytułu

PL

Analiza metod przewidywania zużycia energii w przemyśle a)ęglowym

• Języki publikacji: EN

Abstrakty

EN

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. A comparative analysis of these methods is performed. 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. 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. The proposed models can be applied in automated software systems for predictive control of a production process of a coal mining enterprise.

PL

W pracy uwzględniono model prognozowania zużycia energii elektrycznej przez przedsiębiorstwo przemysłu węglowego w oparciu o trzy metody prognozowania, a mianowicie transformatę falkową, metodę wektorową oraz sieć neutralną rekurencyjną. Przeprowadzana jest analiza porównawcza tych metod. Do wstępnego przetwarzania danych do prognozowania metodami wektorowymi i rekurencyjnymi wybrano metodę Singular Spectrum Analysis. Konstrukcja modelu pozwala na uwzględnienie indywidualnych cech cyklu operacyjnego procesu produkcyjnego oraz wygładzenie składowych i wartości odstających hałasu. Przedstawiono wyniki krótkookresowej prognozy godzinowej na jeden dzień do przodu wraz z porównaniem uzyskanych wartości. Wyniki prognozy krótkookresowego zużycia energii elektrycznej zostały zweryfikowane na podstawie danych rzeczywistych przedsiębiorstwa przemysłu węglowego w celu oceny adekwatności modelu do wartości rzeczywistych. Zaproponowane modele mogą znaleźć zastosowanie w zautomatyzowanych systemach oprogramowania do predykcyjnego sterowania procesem produkcyjnym przedsiębiorstwa górniczego.

Słowa kluczowe

EN

wavelet transform; singular spectrum analysis; power forecasting; power management

PL

transformacja falkowa; analiza widma pojedynczego; prognozowanie mocy; zarządzanie energią

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2022
• Tom: R. 98, nr 9
• Strony: 26--31
• Opis fizyczny: Bibliogr. 31 poz.,rys.

Twórcy

autor

Manusov Vadim Zinovievich

• Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russian Federation

• https://orcid.org/0000-0001-7799-4830

autor

Orlov Dmitry

• Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russian Federation

• https://orcid.org/0000-0002-8310-5312

autor

Karmanov Vitaly Sergeevich

• Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russian Federation

• https://orcid.org/0000-0001-6686-9954

autor

Khusnutdinov Alexander Olegovich

• Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk, 630073, Russian Federation

• https://orcid.org/0000-0002-8421-5011

autor

Kokin Sergey Evgenevich

• Ural Federal University, 19, Mira Street, Yekaterinburg, 620002, Russian Federation

• https://orcid.org/0000-0001-7493-+172X

autor

Safaraliev Murodbek

• Ural Federal University, 19, Mira Street, Yekaterinburg, 620002, Russian Federation

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).