Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

Original language description

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.

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

2023

Confidentiality

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

Name of the periodical

APPLIED ENERGY

ISSN

0306-2619

e-ISSN

1872-9118

Volume of the periodical

2023

Issue of the periodical within the volume

339

Country of publishing house

GB - UNITED KINGDOM

Number of pages

15

Pages from-to

120989-121004

UT code for WoS article

000965062000001

EID of the result in the Scopus database

2-s2.0-85151456461