Advanced long short-term memory-based forecasting of electricity imbalances in the Ukrainian power system: Enhancing accuracy and stability with comparative model analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10258282" target="_blank" >RIV/61989100:27240/25:10258282 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27730/25:10258282

Result on the web

<a href="https://journals.sagepub.com/doi/10.1177/01445987251360272" target="_blank" >https://journals.sagepub.com/doi/10.1177/01445987251360272</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/01445987251360272" target="_blank" >10.1177/01445987251360272</a>

Result language

angličtina

Original language name

Advanced long short-term memory-based forecasting of electricity imbalances in the Ukrainian power system: Enhancing accuracy and stability with comparative model analysis

Original language description

Accurate forecasting of electricity imbalances is critical for maintaining power system stability and improving market efficiency, particularly in systems with high renewable energy penetration. This study proposes an advanced short-term forecasting methodology for the Ukrainian integrated power system using optimized long short-term memory (LSTM) neural networks. The novelty of the approach lies in the integration of automatic hyperparameter tuning and ensemble learning within LSTM architectures, specifically tailored to handle the high non-stationarity and extreme variability in real-world Ukrainian imbalance data. This hybrid structure enables robust performance under volatile market conditions. A comprehensive statistical analysis confirms significant skewness and volatility in hourly imbalance data from March 2022 to September 2024, obtained from NPC &quot;Ukrenergo,&quot; with variation coefficients of 100.14% and 77.37% for positive and negative imbalances, respectively. The proposed LSTM model with a 24-h input window achieved the best standalone accuracy, reducing mean absolute percentage error (MAPE) to 47.49% for positive and 35.96% for negative imbalances. Ensemble configurations (e.g. 4 + 24, 12 + 24) further improved stability, with correlation coefficients R(f, p) reaching 91.9% for positive and 77.83% for negative forecasts. In contrast, benchmark auto-regressive integrated moving average and seasonal auto-regressive integrated moving average models yielded significantly higher errors, with MAPE exceeding 92% and root mean square error values up to 522.15 MW&lt;middle dot&gt;h. Statistical tests, including the Diebold-Mariano and Durbin-Watson tests, validated the superior accuracy and residual independence of the LSTM ensembles. The proposed framework demonstrates substantial improvements in both accuracy and forecast stability, making it a scalable solution for real-time imbalance management in power systems undergoing structural transitions and renewable energy integration. This approach offers practical value for transmission system operators and balancing market participants seeking to enhance predictive performance and reduce imbalance-related penalties.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20700 - Environmental engineering

Project

<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

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

Name of the periodical

Energy Exploration and Exploitation

ISSN

0144-5987

e-ISSN

2048-4054

Volume of the periodical

Neuveden

Issue of the periodical within the volume

July

Country of publishing house

US - UNITED STATES

Number of pages

25

Pages from-to

1-25

UT code for WoS article

001534477000001

EID of the result in the Scopus database

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