Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103327
Title: The impact of data filtration on the accuracy of multiple time-domain forecasting for photovoltaic power plants generation
Authors: Eroshenko, S. A.
Khalyasmaa, A. I.
Snegirev, D. A.
Dubailova, V. V.
Romanov, A. M.
Butusov, D. N.
Issue Date: 2020
Publisher: MDPI AG
Citation: The impact of data filtration on the accuracy of multiple time-domain forecasting for photovoltaic power plants generation / S. A. Eroshenko, A. I. Khalyasmaa, D. A. Snegirev, et al. — DOI 10.3390/app10228265 // Applied Sciences (Switzerland). — 2020. — Vol. 10. — Iss. 22. — P. 1-22. — 8265.
Abstract: The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: AUTOREGRESSION
DATA FILTRATION
DATA PROCESSING
K-NEAREST NEIGHBORS
PHOTOVOLTAIC POWER PLANT
REGRESSION
SHORT-TERM FORECASTING
URI: http://hdl.handle.net/10995/103327
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85096391651
PURE ID: 20139097
67076665-fba5-4388-82ec-0c085179c485
ISSN: 20763417
DOI: 10.3390/app10228265
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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