Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149296" target="_blank" >RIV/00216305:26230/23:PU149296 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0306261923003537" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0306261923003537</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.apenergy.2023.120989" target="_blank" >10.1016/j.apenergy.2023.120989</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

Popis výsledku v původním jazyce

A growing interest in renewable power increases its impact on the energy grid, posing significant challenges to reliability, stability, and planning. Although the use of weather-based prediction methods helps relieve these issues, their real-world accuracy is limited by the errors inherent to the weather forecast data used during the inference. To help resolve this limitation, we introduce the SolarPredictor model. It uses a hybrid convolutional architecture combining residual connections with multi-scale spatiotemporal analysis, predicting solar power from publicly available high-uncertainty weather forecasts. Further, to train the model, we present the SolarDB dataset comprising one year of power production data for 16 solar power plants. Crucially, we include weather forecasts with seven days of hourly history, allowing our model to anticipate errors in the meteorological features. In contrast to previous work, we evaluate the prediction accuracy using widely available low-precision weather forecasts, accurately reflecting the real-world performance. Comparing against 17 other techniques, we show the superior performance of our approach, reaching an average RRMSE of 6.15 for 1-day, 8.54 for 3-day, and 8.89 for 7-day predictions on the SolarDB dataset. Finally, we analyze the effects of weather forecast uncertainty on the prediction accuracy, showing a 23 % performance gap compared to using zero-error weather. Data and additional resources are available at cphoto.fit.vutbr.cz/solar.

Název v anglickém jazyce

Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

Popis výsledku anglicky

A growing interest in renewable power increases its impact on the energy grid, posing significant challenges to reliability, stability, and planning. Although the use of weather-based prediction methods helps relieve these issues, their real-world accuracy is limited by the errors inherent to the weather forecast data used during the inference. To help resolve this limitation, we introduce the SolarPredictor model. It uses a hybrid convolutional architecture combining residual connections with multi-scale spatiotemporal analysis, predicting solar power from publicly available high-uncertainty weather forecasts. Further, to train the model, we present the SolarDB dataset comprising one year of power production data for 16 solar power plants. Crucially, we include weather forecasts with seven days of hourly history, allowing our model to anticipate errors in the meteorological features. In contrast to previous work, we evaluate the prediction accuracy using widely available low-precision weather forecasts, accurately reflecting the real-world performance. Comparing against 17 other techniques, we show the superior performance of our approach, reaching an average RRMSE of 6.15 for 1-day, 8.54 for 3-day, and 8.89 for 7-day predictions on the SolarDB dataset. Finally, we analyze the effects of weather forecast uncertainty on the prediction accuracy, showing a 23 % performance gap compared to using zero-error weather. Data and additional resources are available at cphoto.fit.vutbr.cz/solar.

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

2023

Kód důvěrnosti údajů

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

Název periodika

APPLIED ENERGY

ISSN

0306-2619

e-ISSN

1872-9118

Svazek periodika

2023

Číslo periodika v rámci svazku

339

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

120989-121004

Kód UT WoS článku

000965062000001

EID výsledku v databázi Scopus

2-s2.0-85151456461