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Record W4416839063 · doi:10.3389/fenrg.2025.1719469

Long-term seasonal and interannual performance of a residential photovoltaic system in a temperate continental climate

2025· article· en· W4416839063 on OpenAlexaboutno aff
Beatrice-Georgiana Vuțoiu, Maria-Bianca Tăbăcaru, George-Andrei Beşchea, Gabriel Năstase, George Dragomir, Alin Ionuț Brezeanu, Ștefan-Ioan Câmpean, Codruța-Maria Lodor

Bibliographic record

VenueFrontiers in Energy Research · 2025
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemTemperate climateSeasonalitySolar irradianceRenewable energyGreenhouse gasClimate change

Abstract

fetched live from OpenAlex

This study presents a comprehensive performance evaluation of a residential photovoltaic (PV) system located in Târlungeni, Braşov County, Romania, monitored over a 29-month period from January 2023 to May 2025. The 6.48 kWp system comprises 16 Canadian Solar HiKu6 Mono PERC 405 W modules (module efficiency 20.7%) configured in an East-West orientation and connected to a Huawei SUN 2000-5KTL-M0 inverter (maximum efficiency 98.4%). The analysis focuses on seasonal variations and interannual trends in energy production, self-consumption rates, and grid dependency patterns. Data extracted from the Huawei FusionSolar energy management platform reveals a significant increase in solar yield from 47.18 kWh in January 2023 (during the system commissioning phase with partial operational period) to 286.76 kWh in January 2025, reflecting full system maturity. Seasonal patterns show peak solar production of 654.83 kWh in August 2023, with substantial monthly variation driven by climate dynamics typical of temperate continental regions. The self-consumption rate averaged 27%–36% across the monitoring period, while energy independence levels ranged from 17%–22% in winter months to 50%–58% in summer months. Grid dependency followed inverse patterns, with winter months requiring 77%–82% grid import versus 42%–48% in summer. The coefficient of variation for monthly solar yield was 52%, reflecting high seasonal variability characteristic of the temperate continental climate. These findings provide valuable insights for residential PV system planning in temperate continental climates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.291
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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