Wind-Power Intra-day Statistical Predictions Using Sum PDE Models of Polynomial Networks Combining the PDE Decomposition with Operational Calculus Transforms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10245223" target="_blank" >RIV/61989100:27240/21:10245223 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-49336-3_8" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-49336-3_8</a>
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Wind-Power Intra-day Statistical Predictions Using Sum PDE Models of Polynomial Networks Combining the PDE Decomposition with Operational Calculus Transforms
Original language description
Chaotic processes in complex atmospheric circulation and fluctuation waves in local conditions cause difficulties in wind power prediction. Physical models of Numerical Weather Prediction (NWP) systems produce only coarse 24-48-h prognoses of wind speed, which are not entirely assimilated to local specifics and usually delayed to be produced every 6-h. Artificial Intelligence (AI) techniques can process daily forecasts or calculate independent statistical predictions using historical time-series in a few-hour horizon. The presented unconventional neuro-computing method elicits Polynomial Neural Network (PNN) structures to decompose the n-variable Partial Differential Equation (PDE), into a set of node-converted sub-PDEs. The inverse Laplace transformation is applied to the node produced rational terms, using Operational Calculus (OC), to obtain the originals of unknown node functions. The complete composite PDE model includes the sum of selected sub-PDE solutions, which allow detail representation of complex weather patterns. Self-adapting statistical models are developed using a specific increased inputs->-output time-shift to represent the current local near-ground conditions for predictions in the trained time-horizon of 1-12 h. The presented multi-step procedure forming statistical AI models allow more accurate intra-day wind power predictions than processed middle-scale numerical forecasts.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 1179
ISBN
978-3-030-49335-6
ISSN
2194-5357
e-ISSN
2194-5365
Number of pages
10
Pages from-to
72-82
Publisher name
Springer
Place of publication
Cham
Event location
Bhópál
Event date
Dec 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—