Data-complexity-based selection of electricity-loss forecasting models in 0.4 kV distribution networks

Khilola Z. Nazirova

Энергия ва ресурс тежаш муаммолари · 2026-yil

Annotatsiya

Relevance: the growing volume of AMI and smart-meter data makes model selection a separate methodological problem in electricity-loss forecasting. A single forecasting method does not provide uniform accuracy across time series that differ in sample size, variability, seasonality, and multivariate structure. Aim: to determine rational applicability ranges of Regression, GMDH, ARIMA, MLP and DBSCAN-MLP forecasting models using an integral criterion that combines data volume and relative variability. Methods: the study used 912 real operational observations collected in 2021-2023, correlation analysis, Fisher transformation and Z-testing, polynomial regression, ARIMA, GMDH, MLP, DBSCAN-MLP, and MAPE-based error assessment. Data complexity was characterized by . Results: experiments on 20 univariate and 20 multivariate datasets yielded empirical model-selection boundaries. Overall MAPE values were 5.01, 3.15, 5.05, 2.58 and 2.10% for Regression, ARIMA, GMDH, MLP and DBSCAN-MLP, respectively. The hybrid-model threshold was θ≥4.2 for univariate and θ≥3.9 for multivariate data.

Maqola ma’lumotlari
MualliflarKhilola Z. Nazirova
JurnalЭнергия ва ресурс тежаш муаммолари
Nashriyot«Ўзбекэнерго» АО, ТДТУ, Ўзбекистон Республикаси ФА, Энергия ва ресурслар тежаш илмий-амалий ўқув маркази, «Ўздавэнергоназорат» давлат инспекцияси, «Энергия тежамкорлиги ва қайта тикланувчи энергия манбалари» илмий-тадқиқот лабораторияси
ISSN2091-5985, 2181-1946
Nashr sanasi2026-10-13
Son3
Betlar194-202
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-10-22

Maqolaning asl sahifasi

Kalit so‘zlar

потери электроэнергии, electricity losses, elektr energiyasi isroflari

Ilmiy soha

Texnika

Iqtibos keltirish

APAKhilola Z. Nazirova (2026). Data-complexity-based selection of electricity-loss forecasting models in 0.4 kV distribution networks. Энергия ва ресурс тежаш муаммолари, (3), 194-202. https://energy.tdtu.uz/index.php/journal/article/view/426
GOST (OAK)Khilola Z. Nazirova Data-complexity-based selection of electricity-loss forecasting models in 0.4 kV distribution networks // Энергия ва ресурс тежаш муаммолари. – 2026. – № 3. – P. 194-202.
MLAKhilola Z. Nazirova. “Data-complexity-based selection of electricity-loss forecasting models in 0.4 kV distribution networks.” Энергия ва ресурс тежаш муаммолари, (3), 194-202, 2026. https://energy.tdtu.uz/index.php/journal/article/view/426
ChicagoKhilola Z. Nazirova. “Data-complexity-based selection of electricity-loss forecasting models in 0.4 kV distribution networks.” Энергия ва ресурс тежаш муаммолари, (3), 194-202 (2026). https://energy.tdtu.uz/index.php/journal/article/view/426
HarvardKhilola Z. Nazirova (2026) ‘Data-complexity-based selection of electricity-loss forecasting models in 0.4 kV distribution networks’, Энергия ва ресурс тежаш муаммолари, (3), 194-202. https://energy.tdtu.uz/index.php/journal/article/view/426
BibTeX
@article{khilolaznazirova2026,
  title = {Data-complexity-based selection of electricity-loss forecasting models in 0.4 kV distribution networks},
  author = {Khilola Z. Nazirova},
  journal = {Энергия ва ресурс тежаш муаммолари},
  year = {2026},
  number = {3},
  pages = {194-202},
  url = {https://energy.tdtu.uz/index.php/journal/article/view/426}
}

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