.png){kind=icon}
Return
PV System Performance Analysis and Forecasting Using Deep Learning and Statistical Methods
.png){kind=icon}
OA
AI
DOI：10.3390/en19071739.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study investigates the long-term performance degradation and forecasting of three silicon-based photovoltaic technologies—polycrystalline (pc-Si), monocrystalline (mc-Si), and amorphous silicon (a-Si)—using a seven-year dataset (2015–2021) from a semi-arid climate. Degradation rates are quantified through seasonal-trend decomposition and Arrhenius analysis, revealing distinct mechanisms: pc-Si exhibits the lowest annual degradation (0.36%/year), followed by a-Si (0.57%/year), while mc-Si shows the highest (0.77%/year), with a notable thermal annealing effect partially compensating degradation in a-Si. For forecasting performance ratio, four models are compared, where long short-term memory networks achieve the highest accuracy by capturing nonlinear temporal dependencies, while SARIMA offers robust, interpretable results with lower complexity. Beyond predictive performance, the study establishes links between model behavior and underlying physical processes such as degradation and annealing, and analyzes prediction uncertainty in relation to temperature variability and dust accumulation. These findings highlight trade-offs between accuracy, interpretability, and deployment feasibility, providing a framework for PV performance forecasting under univariate, semi-arid conditions, with future work directed toward multivariate, physics-informed approaches across broader technologies and climates.
Keywords:
performance ratio
Facebook prophet
SARIMA
MLP
LSTM
silicon PV technology
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.2
Papers:
1.5W
Citations:
14.2W
Organization
Cited Papers
Short-term PV power forecasting based on time series expansion and high-order fuzzy cognitive maps
PREAI
ShareSaveShareSaveLife cycle assessment and energy pay-back time of advanced photovoltaic modules: CdTe and CIS compared to poly-Si.png)
ENERGY
ENERGYIF9.4
PREAI
ShareSaveModelling and experimental investigations of microcracks in crystalline silicon photovoltaics: A reviewRENEWABLE ENERGY
RENEWABLE ENERGYIF9.1
PREAI
ShareSaveMachine learning assisted prediction for hydrogen production of advanced photovoltaic technologiesDeCarbonOAAI
DeCarbonIF0
OAAIShareSaveShareSaveShareSave
View more