# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 1 Introduction  
Recently, research on alternative energy resources has grown in popularity because traditional energy sources are environmentally unfriendly and non-sustainable. Moreover, there is an ever-growing global energy crisis, since economic and technological advancements are highly dependent on the availability of energy that is essential to industrialization and urbanization worldwide. Conversely, the progressive growth of global inhabitancy has exacerbated the severity of the global energy shortages. According to [1], electricity demand is anticipated to grow by up to 70%. In the 20 th century, fossil fuels were acknowledged as the main sources of electrical energy generation, and they continue to play an important role today. Despite this fact, the prolonged consumption of fossil fuel supplies, which are already in short supply, adversely affects global climate change (greenhouse effect or global warming) [2, 3] and is hazardous to global health [4]. More recently, solar energy has been widely utilized in photovoltaic (PV) power generation. The global PV power capacity may exceed 1700 GW by 2030 [5]. Thus, the generation of PV power is known as another promising renewable energy source because it offers more benefits, including reducing the use of fossil fuels and helping power systemoperators to achieve their peak load demand [6]. However, the forecastability of solar PV output is heavily influenced by weather conditions, such as rainy, cloudy, and sunny days; sudden weather changes; snowy days; and other weather types, which present challenges for system administrators. Henceforth, PV power generation must be reliable in terms of accuracy for effective grid function [7; 8]. Thus, commercial electric power companies are having difficulties in delivering reliable and safe electricity to their clients because electric power systems require accurate forecasting models to plan their operations. The demand patterns for electricity also include time, the economy, and social and environmental factors [9; 10]. Solar irradiance forecasting assists in the incorporation of solar PV plants into the electricity grid, the scheduling of energy storage systems, and energy transmission optimization, which reduces energy loss [11]. It also reduces reserve capacity and cost generation, thus preventing electrical energy system disruption [12], making it easier to predict PV power generation. Over the years, solar irradiance forecasting has gained a lot of interest from both academics and businesses due to the abundant and limitless nature of solar energy that allows it to provide sustainable power globally. On average, the Earth receives 1367 W/m2 of solar radiation per day, which can yield 1.74 \(\times\) 10\({}^{17}\) W in a year. Figure 1 illustrates the potential distribution of solar energy globally, inferencing the limitless solar energy supply on Earth. Thus, solar energy is shown to be the best alternative option to ensure the availability of energy sources for industrial, commercial, and residential use [13]. The main types of power forecasting models are (1) short-term models, which forecast up to 1 day/week in advance; (2) medium-term models, which forecast up to 1 year in advance; and (3) long-term models, which forecast more than 1 year in advance and are mainly utilized to develop electricity power distribution and power supply systems [15; 16; 17]. Sudden changes in solar irradiance, known as ramp events, are relevant for extremely short-term and very short-term forecast horizons. Furthermore, if changes in solar irradiance happen too quickly and strongly, PV power could become less reliable and reduce in quality. Therefore, the results of short-term forecasting may be utilized to determine the most effective PV power ramp rates [18]. Moreover, forecasting for the long term and medium term enables operational enhancement and market engagement [19]. Day-ahead solar irradiance estimates have been shown to enhance yearly energy consumption for commercial operations in building micro grids.