Detail publikace

Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

POLÁŠEK, T. ČADÍK, M.

Originální název

Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

solar power forecasting, photovoltaic dataset, prediction uncertainty, machine learning model

Autoři

POLÁŠEK, T.; ČADÍK, M.

Vydáno

27. 3. 2023

Nakladatel

Elsevier

Místo

Oxford

ISSN

0306-2619

Periodikum

APPLIED ENERGY

Ročník

2023

Číslo

339

Stát

Spojené království Velké Británie a Severního Irska

Strany od

120989

Strany do

121004

Strany počet

15

URL

https://www.sciencedirect.com/science/article/pii/S0306261923003537

BibTex

@article{BUT185047,
  author="Tomáš {Polášek} and Martin {Čadík}",
  title="Predicting Photovoltaic Power Production using High-Uncertainty Weather Forecasts",
  journal="APPLIED ENERGY",
  year="2023",
  volume="2023",
  number="339",
  pages="120989--121004",
  doi="10.1016/j.apenergy.2023.120989",
  issn="0306-2619",
  url="https://www.sciencedirect.com/science/article/pii/S0306261923003537"
}