## 1 Introduction  
By 2020, the energy consumption within developing countries is expected to double from 2015 levels [1]. This additional generation capacity, especially if based on non-renewable resources, will have negative consequences to Earth's climate, which in turn adds urgency to the development and integration of renewable energy technologies. As such, the International Energy Agency (IEA) proposed a hi-Ren Scenario with the slower deployment of nuclear, and carbon capture and storage technologies, but the more rapid deployment of renewables, notably solar and wind energy, to prevent increasing levels of CO\({}_{2}\) emissions [2]. The average annual growth rate of solar photovoltaic (PV) was 46.2% from 1990 to 2013, with renewable energy providing 13.8% of the worldwide total primary energy demand in 2014 [3]. The growth of renewables is particularly aggressive in China, which invested $83.3 billion in renewable energy in 2014, and leads other countries in this area. According to the results from the IEA hi-Ren Scenario, China could account for half of the global CO\({}_{2}\) emission reduction by 2050 due to its PV deployment. By 2015, China's total solar PV installed capacity reached 35 GW and is expected to reach 634 GW by 2030 and 1738 GW by 2050, respectively, to achieve its CO\({}_{2}\) emission reduction targets [4; 5]. However, the stochastic nature of ground solar irradiation caused by various weather conditions leads to similar fluctuations in the output power of solar PV systems. The resource variability affects integration costs and energy price competitiveness in large-scale, grid-connected PV plants, small-scale distributed PV systems and stand-alone PV systems. In high-penetration power feeders and micro-grids, the solar resource variability leads to significant changes in net load curves [6; 7]. Increasing grid and market penetration of PV systems in recent years has resulted in challenges for dispatch control of ancillary generation at both utility and operator levels with implications to spin reserve levels and potentially reducing overall system reliability and stability [8; 9; 10; 11]. Low-cost mitigation approaches for coping with variable generation include the development of accurate solar power forecasting methods [12; 13; 14]. Over the past decade, research in the field of solar forecasting has grown rapidly, with developed methodologies for forecasting the evolution of solar radiation over time and space falling into several different categories [15; 16; 17]:  
* Sky images and satellite imagery based methods. The former methods are mainly used for intra-hour forecasting, while the latter features forecast horizons of tens of minutes to up to 6 h [18; 19; 20; 21; 22; 23; 24]. * Statistical learning based methods--work best for the intra-hour forecast horizons, but can also be applied for longer forecasting, up to 2 or 3 h, when combined with other methods [25; 26; 27; 28; 29]. * Numerical Weather Prediction (NWP) based methods. NWP systems have been utilized for forecasting applications for many years and perform best for the time horizons from 6 h to 2 weeks [30; 31; 32; 33]. * Climatology based methods. These methods are mainly used for forecasting and evaluating the solar energy for time horizons larger than 6 h [34; 35; 36]. The wide variety of weather conditions that are possible leads to high volatility in solar radiation and makes it harder to recognize the changing patterns of irradiance and corresponding PV power output. This could be particularly obvious in day-ahead short-term (DAST) solar PV power forecasting. To show the challenges of solar PV power forecasting, [PERSON] analyzed the characteristics of PV power and load in different daily weather conditions; unsurprisingly, the simulations show that the solar power exhibits a higher volatility and larger forecasting errors on cloudy and rainy days [37].