## 1 Introduction  
The theoretical efficiency of 29.43% in silicon-type solar cells is due to the relationship between the bandgap of the semiconductor material and the solar spectrum [1]. Currently, the world record for efficiency of a general silicon PV cell is 26.7% [2]. To further improve efficiency, it is necessary to utilize a wider range of the solar spectrum to reduce spectral mismatch. As a solution to this problem, there is a multijunction solar cell formed by laminating single-junction solar cell layers made of different materials. For example, in three-junction cells of InGaP/GaAs/InGaAs fabricated by SHARP, light of a wide wavelength range can be absorbed, leading to a high efficiency of 44.4% under concentration and 37.9% under nonconcentration [3; 4]. The required energy is determined by the demands of the user side. To stabilize the supply of electric power, it is necessary to constantly match the required energy and supplied energy. Because PVis greatly affected by weather conditions such as water content, and turbidity, it is difficult to balance supply and demand with using PV. To solve these problems, it is necessary to estimate power with high accuracy. Recently, various PV output forecasting models have been suggested. One well-known model is the power temperature coefficient model as described in [5], which assumes that the maximum power output is linear with irradiance at a given temperature and that the power temperature coefficient is a constant [6; 7; 8]. Among them, an output forecasting using an artificial neural network was proposed [9; 10; 11; 12; 13; 14]. However, scattering and absorption of the solar spectrum by atmospheric conditions is not considered in these models. The outdoor performance of multijunction solar cells reflects the influence of this spectrum mismatch in the literature [15; 16; 17; 18; 19; 20]. This is because the solar spectrum in the real environment is different from the standard reference spectrum. Each sub-cell in multijunction solar cells absorbs different wavelength of the solar spectrum, and the photocurrent changes with spectral variation. The output current in multijunction solar cells is limited by the smallest sub-cell, because the sub-cells are electrically connected in series; this phenomenon is called spectrum-mismatch. In this paper, we propose a new PV output forecasting method including the extraction of atmospheric parameters and the calculation of the solar spectrum based on [PERSON]'s spectral model [21], named the \"Miyazaki Spectrum-to-Energy method\" (MS2E). [PERSON]'s spectral model has been adopted as the reference solar spectrum and has gained high trust. However, there are two problems with this spectral model. One is that atmospheric parameters are constants. Under standard test conditions (STC), for example, the aerosol optical depth (AOD) in a vertical path at 500 nm wavelength is 0.084, and the precipitable water amounts to 1.416 cm [22]. However, atmospheric parameter fluctuation greatly depends on place and season. [PERSON]'s spectrum model is also adapted only for clear skies without clouds [21]. Spectrum scattering differs between sunny and cloudy conditions, so it is preferable to calculate the solar spectrum in consideration of the cloud conditions such as overcast skies and the possibility of rain. To improve forecast accuracy, the fluctuation of atmospheric parameters and an overcast spectral model that assumed full cloud cover were applied to the method. The all-weather spectrum, including seasonal variability, was calculated by the linear combination of the clear spectrum and the overcast spectrum. After that, the short-circuit current (\(I_{\text{sc}}\)), open-circuit voltage (\(V_{\text{oc}}\)), and fill factor (\(FF\)) of various PVs were calculated, and the maximum power (\(P_{\text{out}}\)) was estimated. The accuracy of our PV output forecasting method was verified by comparing it with the measured value at the University of Miyazaki.