The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

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Solar energy and other clean energy are emerging and growing rapidly in the globe nowadays. Solar energy with less carbon emission is renewable and clean energy for our living environment. Solar energy can be converted to electricity in photovoltaic (PV) devices, solar cells, or solar thermal/electric power plants.It is a current trend that solar energy becomes the important renewable energy. This special issue addresses the role of the development of solar energy. The themes include dyesensitized solar cells (DSSCs), organic solar cells (OSCs), copper indium gallium diselenide (CIGS), zinc, crystalline silicon solar cells, light-emitting diode (LED), semiconductor sensors, photovoltaic generation system (PVGS), solar cell applications, solar cell, and LED development trend. From 60 submissions, 33 papers are published in this special issue. Each paper was reviewed by at least two reviewers and revised according to review comments. -Sensitized Solar Cells (DSSCs). The design of lightabsorbent sensitizers with sustainable and environmentfriendly material is one of the key issues for the future development of dye-sensitized solar cells (DSSCs). In this work, a series of organic sensitizers incorporating alkoxysubstituted triphenylamine (tpa) donors and binary -conjugated bridges were investigated using density functional theory (DFT) and time-dependent DFT (TD-DFT). Molecular geometry, electronic structure, and optical absorption spectra are analyzed in the gas phase, chloroform, and dimethylformamide (DMF) solutions. In S. Wei et al. 's paper, the authors show that properly choosing the heteroaromatic atoms and/or adding one more alkoxy-substituted tpa group can finely adjust the molecular orbital energy. In H. J. Jo et al. 's paper, a new multicarbazole based organic dye (C2A1, C2S1A1) with a twisted structure was designed and synthesized, and the corresponding dye (C1A1) without the twisted structure was synthesized for comparison. They were successfully applied in dyesensitized solar cells (DSSCs). The results showed that the nonplanar structure of C2A1 and C2S1A1 can efficiently retard the dye aggregation and charge recombination. TiO 2 compact layers are used in dyesensitized solar cells (DSSCs) to prevent charge recombination between the electrolyte and the transparent conductive 2 International Journal of Photoenergy substrate (indium tin oxide, ITO; fluorine-doped tin oxide, FTO). In H. C. Chang et al. 's paper, the authors presented that thin TiO 2 compact layers are deposited onto ITO/glass by means of radio frequency (rf) magnetron sputtering, using deposition parameters that ensure greater photocatalytic activity and increased DSSC conversion efficiency. The photoinduced decomposition of methylene blue (MB) and the photoinduced hydrophilicity of the TiO 2 thin films are also investigated. In H.-C. Chang et al. 's paper, the authors presented that a series of compact TiO 2 layers are prepared using radio frequency (rf) reactive magnetron sputtering. The films are characterized using X-ray diffraction (X...

This paper proposes a novel methodology for forecasting of one hourly global solar irradiance (GSI). This methodology is a combination of k-NN decompotition method and artificial neural network (ANN) algorithm modelling. The k-NN Decomposition-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN Decomposition as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN Decomposition-ANN model and the root-mean-square error (RMSE) is 20 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the k-NN Decomposition-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy.