Photovoltaic Power intra- and day-ahead Predictions with Differential Learning producing PDE-modular Models based on the Node L-transform Derivatives

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250149" target="_blank" >RIV/61989100:27240/22:10250149 - isvavai.cz</a>

Result on the web

<a href="https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/ep.13977" target="_blank" >https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/ep.13977</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ep.13977" target="_blank" >10.1002/ep.13977</a>

Result language

angličtina

Original language name

Photovoltaic Power intra- and day-ahead Predictions with Differential Learning producing PDE-modular Models based on the Node L-transform Derivatives

Original language description

Predictions of Photovoltaic (PV) energy supply, based on data statistics of weather and PV records, are required in short-intra and day-ahead planning of PV plant operations. Numerical Weather Prediction (NWP), based on physical consideration, can simulate the progress of local cloudiness, although their progno-ses are usually delayed by a few hours and are not provided with the quality desired by PV operators. Differential Polynomial Neu-ral Network (D-PNN) is an unconventional hybrid regression technique, based on a novel learning strategy and able to resolve some problems in modelling patterns in high-dynamical and chaotic non-deterministic systems. D-PNN splits the general Par-tial Differential Equation (PDE) of the kth order into a summation form of two-variable PDEs of a predetermined low order. PDE derivatives are transformed and separated into the Laplace coun-terparts, from which the unknown node originals are restored using the inverse L-operation. D-PNN searches for the best 2-input couples, inserting node by node into its changeable tree structure, to expand the modular sum model by usable PDE components. The PDE-modularization enhances the D-PNN capability in modelling high uncertainty in weather patterns in the ground-layer atmosphere, analogous to NWP. The designed PV intra- and day-ahead prediction schemes, using hourly in-creasing and fixed-time prediction horizons, were compared with different computing approaches based on differential, probabilis-tic and statistical learning. Machine learning (ML) models calcu-late the output PV power in each single hour separately or in an all-day processing series with trained input delay. The results are analyzed and interpreted for each day and prediction hour. The quantitative results for both prediction procedures are compara-ble, showing the average intraday 11.6% and all-day 9.6% (or rather 12.3% resp.) inaccuracy with respect to the daily peak power. Training data samples with spatial overlap in the regional scope and ML initialized similarity intervals are related to the fixed-day or hourly increased input-output delay. This approach can compensate for unpredictable midterm variances in local weather patterns (caused by terrain or convection anomalies) and possible errors in model initialization. PV power was converted to and restored from the Clear Sky Index (CSI) in the modelling input/output, which represents its relative values, disregarding the actual solar cycle time.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Environmental Progress and Sustainable Energy

ISSN

1944-7442

e-ISSN

1944-7450

Volume of the periodical

42

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1-14

UT code for WoS article

000843623500001

EID of the result in the Scopus database

—