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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Environmental Progress and Sustainable Energy

ISSN

1944-7442

e-ISSN

1944-7450

Svazek periodika

42

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

000843623500001

EID výsledku v databázi Scopus

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