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.

中文翻译：

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应用太阳辐照度预测的新型协作多输入多层感知器神经网络性能分析

在光伏 (PV) 能源系统中，以更高的精度和泛化能力预测太阳辐照度是一项挑战。气象参数对太阳辐照度影响很大，导致间歇性和随机性。使用单一神经网络模型进行预测并没有足够的泛化能力来实现太阳辐照度的最优预测。本文提出了一种新颖的协作多输入多层感知器神经网络 (CMMLPNN)，以减轻与泛化和气象效应相关的问题。作者基于与多层感知器神经网络相关的两个输入、三个输入、四个输入、五个输入和六个输入的合并，开发了一个建议的预测神经网络模型。在提出的预测模型（CMMLPNN）中，作者克服了基于气象参数的差异。五个多输入多层感知器神经网络的融合导致更好的泛化能力。一些单独的基于多层感知器神经网络的预测模型在某些情况下表现优异，但不能保证泛化能力，并且受到气象天气条件的影响。所提出的 CMMLPNN（协同多输入多层感知器神经网络）具有更好的预测精度和泛化能力。因此，所提出的预测模型优于其他基于神经网络的预测模型和现有模型。一些单独的基于多层感知器神经网络的预测模型在某些情况下表现优异，但不能保证泛化能力，并且受到气象天气条件的影响。所提出的 CMMLPNN（协同多输入多层感知器神经网络）具有更好的预测精度和泛化能力。因此，所提出的预测模型优于其他基于神经网络的预测模型和现有模型。一些单独的基于多层感知器神经网络的预测模型在某些情况下表现优异，但不能保证泛化能力，并且受到气象天气条件的影响。所提出的 CMMLPNN（协同多输入多层感知器神经网络）具有更好的预测精度和泛化能力。因此，所提出的预测模型优于其他基于神经网络的预测模型和现有模型。