Abstract
To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.
Highlights
In recent trends, solar energy is an inevitable renewable energy source to avoid environmental hazards, climatic changes, etc
It creates pressure on the power system engineers. It requires accurate forecasting of future solar irradiance to eliminate the problem of solar energy system irregularity, because solar power production from the solar PV system highly depends on solar irradiance
Remark: the results revealed that the Levenberg Marquardt (LM) training function associated with ANN achieves good results compared with other training functions
Summary
Solar energy is an inevitable renewable energy source to avoid environmental hazards, climatic changes, etc. Solar energy is receiving a center of attraction because of the pollution-free renewable resources, and the vast potential is available to supply power to the entire world. Solar energy has many advantages and special features, one major issue for the solar energy system is irregular in nature and volatile. It creates pressure on the power system engineers. It requires accurate forecasting of future solar irradiance to eliminate the problem of solar energy system irregularity, because solar power production from the solar PV system highly depends on solar irradiance. Effect of shift measurement and shift noise and solution: Measurement shift and noise cause the variability of the solar irradiance forecasting. Onsite measurement datasets with continuous maintenance, commissioning, sensor calibration, quality check, and data evaluation can prevent the uncertainty related to the measurement shift and noise
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