# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 2 The Impact Factor of Solar Power Forecasting
### Forecasting Horizon  
Forecasting models for solar energy can be classified based on their forecasting horizon. A power generation operator must be aware of future power generation and consumption demands. The use of solar irradiance forecasting relies heavily on the types of forecasting horizon to ensure its efficient installation of various applications, including the execution of PV power plants. Specifically, the operation of the grid requires varied time horizons to achieve grid stability, spinning reserve scheduling, and unit commitment [51]. There are three main types of forecasting horizons: (1) short-term, (2) medium-term, and (3) long-term horizons. In addition, limited studies included a fourth type, which is referred to as \"extremely short-term forecasting\". Currently, there is no general taxonomy available for forecasting horizon; therefore, the list is presented as follows.  
* Intra-hour forecasting, also known as nowcasting, is a very short-term forecasting horizon that ranges from 1 min to many minutes in advance and is often used in electricity pricing, bidding, real-time power system dispatch monitoring, and peak load matching [52].
* Short-term forecasting horizons range from 1 or several hours to 1 day or week in advance [2] and are critical for optimal unit commitment, rotating reserve control, and analyzing sales/purchase contracts between multiple enterprises. Therefore, they facilitate the development of a PV-integrated energy management system and increase grid security.
* Medium-term forecasting horizons range from 1 month to 1 year in advance [53] and are efficient for modeling the maintenance schedule of solar power plants developed using transformers and other facilities that incur the least amount of loss [54].
* Long-term forecasting horizons range from 1 year to 10 years in advance. This distance is ideal for designing long-term plans that allow the creation of effective solar power facilities and global management, such as site selection, required to develop a PV power plant and ensure transmission, operation, and distribution of solar energy [55]. However, it is not perfect for long-term forecasting due to its inability to predict long-term weather changes. However, it is still deemed to be the most suitable model for designing schedule plans, setting prices, and selecting site strategies [56; 57].  
The forecasting horizon is one of the most influential factors for forecasting models in terms of accuracy, even when its input features remain constant [58]. In such a case, ref. [59] utilized SVM to generate a very short-term forecasting model. Their findings included a decrease in forecasting accuracy for a forecasting horizon ranging from 5 to 30 min from 96 to 64.6%, respectively, for similar datasets. The growth of statistical data at shorter time scales complicates the training process rather than improving accuracy. Several interrelated variables affect the accuracy of solar irradiance forecasting, such as very short time scales (including one sec or one min period), cloud conditions, and intensity of solar radiation towards the Earth. It is critical to focus on types of cloud classification and forecasts to achieve effective solar irradiation forecasting. However, existing researchers often do not consider cloud conditions when developing their models, which eventually leads to poor forecast accuracy [60; 61; 62].