An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants
نویسندگان
چکیده
Incorporating solar energy into a grid necessitates an accurate power production forecast for photovoltaic (PV) facilities. In this research, output PV was predicted at hour ahead on yearly basis three different plants based polycrystalline (p-si), monocrystalline (m-si), and thin-film (a-si) technologies over four-year period. Wind speed, module temperature, ambiance, irradiation were among the input characteristics taken account. Each plant parameter. A deep learning method (RNN-LSTM) developed evaluated against existing techniques to of selected plant. The proposed technique compared with regression (GPR, GPR (PCA)), hybrid ANFIS (grid partitioning, subtractive clustering FCM) machine (ANN, SVR, SVR (PCA)) methods. Furthermore, LSTM structures also investigated, recurrent neural networks (RNN) 2019 data determine best structure. following parameters prediction accuracy measure considered: RMSE, MSE, MAE, correlation (r) determination (R2) coefficients. comparison all other approaches, RNN-LSTM had higher minimum (RMSE MSE) maximum (r R2). p-si, m-si a-si showed lowest RMSE values 26.85 W/m2, 19.78 W/m2 39.2 respectively. Moreover, found be robust flexible in forecasting considered plants.
منابع مشابه
The Road Ahead for Solar PV Power
Over the past decade, solar photovoltaic (PV) power has experienced dramatic deployment growth coupled with substantial decreases in system prices. This article examines how solar PV power is currently positioned in the electricity marketplace and how that position is likely to evolve in the foreseeable future. We first assess the current cost competitiveness of solar PV in select U.S. location...
متن کاملmortality forecasting based on lee-carter model
over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...
15 صفحه اولAn intelligent maximum power point tracking method based on extension theory for PV systems
The purpose of this paper is to propose a novel maximum power point tracking (MPPT) technique to fully utilize photovoltaic (PV) array output power that depends on solar insolation and ambient temperature. The proposed intelligent MPPT algorithm based on extension theory can automatically adjust the step size to track the PV array maximum power point (MPP). Compared with the conventional fixed ...
متن کاملComparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting
Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dep...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Energies
سال: 2022
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en15062243