# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 6 The Challenge and Future Direction of Solar Energy Forecasting Using Deep Learning  
Deep learning has already been used to make solar energy projections, and there has been a lot of research into this topic. Deep learning-based forecasting models, on the other hand, confront the two below real-world issues. Solving these issues will further increase the accuracy of the deep learning forecasting model [178]. Theoretical issues: Theoretical concerns related to deep learning are generally exhibited in two areas: statistics and calculating abilities. Any non-linear function can be represented by a shallow or deep network. The deep learning model outperforms the shallow model in non-linear representation. When it comes to solar and renewable energy forecasts, it is necessary to understand how difficult predicting samples can be, as well as how many training samples and how much processing power are required to build the deep learning network required to train these forecasting samples. Modeling problems: These problems are related to dealing with large volumes of data. Accordingly, the extracted feature data from large-scale forecasting samples will become more relevant in parallel to the evolution of deep learning. The main objective of deep learning is to improve forecasting accuracy by learning more relevant information directly and spontaneously. The mechanism of deep learning models consists of hidden neurons with up to 6, 7, or even 10 layers, which allow more efficient feature learning than other shallow learning models. Moreover, deep learning makes it easier to learn data from solar energy time series through layer-by-layer feature learning. Despite this fact, developing an efficient hierarchical model with strong feature learning is challenging. Thus, the key future directions for deep learning-based solar energy forecasting models are highlighted as follows:  
1. Data augmentation: The use of data augmentation techniques make deep learning, and especially deep networks, work more effectively; therefore, it is always recommended to develop novel methods for generating data that will better serve this purpose. This work also paves the way for future research in a variety of areas, including photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium- and long-term, etc., by expanding and embracing the modern technique suggested in this work through data generation; in contrast, most studies of photovoltaic power, wind power, and energy consumption concentrate on the short-term forecast, disregarding medium- and long-term forecasts due to a lack of training data for models. 2. Probability forecasting: Many studies on deterministic renewable energy forecasting have been published. However, there is still limited work performed on deep learning-based probabilistic forecasting models. For solar energy time series data, the probabilistic forecasting model can be used to objectively quantify the level of uncertainty. Hence, it is essential to the operation and administration of electric power and energy systems daily [178]. 3. Unified Predictive Model: Solar and renewable energy data have a variety of deep features in different seasons and meteorological and topographic situations. Accordingly, the forecasting model varies based on the situation. However, it is difficult to determine whether a forecasting model could work effectively with different types of data because of the unique solar energy datasets used in existing research. 4. Features Extraction Method: The use of feature extraction is important for effective solar energy data forecasting; therefore, it is always recommended to develop a novel future extraction method for deep forecasting data that will better serve this purpose. Currently, there is only one deep learning strategy that is used for feature extraction in the current deep learning forecasting model. Thus, it is recommended to develop feature extraction for deep forecasting features by combining different deep learning algorithms for future work.