Репозиторий КазНИТУ

Power Generation Forecasting From Solar Plants Based on Weather Conditions for Kazakhstani Region

Авторы:

Omirgaliyev, R. (Соавтор)

Kazbekov, B. (Соавтор)

Zhakiyev, N. (Соавтор)

Zhakiyeva, S. (Соавтор)

Assylkhan, A. (Соавтор)

Издательство:

Institute of Electrical and Electronics Engineers Inc.

Дата издания

03.07.2024

Область исследования

Инжиниринг и технологии

Тип исследования

Прикладное

Аннотация (краткое описание)

This paper presents a comprehensive approach to forecast solar power generation in the Kazakhstani region, leveraging historical and weather data. The main challenge with solar power lies in its dependency on weather conditions, which significantly impacts day-ahead output planning and grid operations. To address this, we have employed advanced timeseries forecasting models like ARIMA, Facebook Prophet, and XGBoost to predict the power output for the next day. By utilizing the real data from two solar plants located in Kazakhstan and analyzing region's historical weather patterns, our proposed model were carefully validated with walk-forward model validation approach. Obtained results highlight superior performance of XGBoost model, outperforming plant dispatcher's predictions and demonstrate the potential of utilizing machine learning in enhancing the reliability of solar power forecasts. This research provides practical insights for both academics and practitioners, including grid managers and plant dispatchers, aiming to optimize solar power integration and reliable operation of the power grid.

Соответствие целям устойчивого развития (ЦУР)

Недорогая и чистая энергия

URI

https://www.scopus.com/record/display.url?eid=2-s2.0-85199463809&origin=resultslist

DOI

10.1109/icSmartGrid61824.2024.10578094

Тип публикаций

Материалы конференции

Сведения о финансировании

This research is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (No. AP19579354).

Дата подачи заявки

01.01.1970