# A Survey of Machine Learning Models in Renewable Energy Predictions
## 1 Introduction
### Review Studies of Applying Machine-Learning Techniques to Renewable Energy  
Table 1 lists some recent survey studies of applying machine-learning techniques to renewable-energy predictions. [PERSON] et al. [8] reviewed the literature on the topic of photovoltaic power prediction by using artificial intelligence technology, machine-learning techniques, deep learning, and hybrid methods. The author pointed out that by using numerical weather predictions with feature extraction and deep-learning techniques to achieve long-term photovoltaic-power-generation prediction, long short-term memory networks, convolutional neural networks, and recurrent neural networks, they were able to estimate the time-dependence data in photovoltaic power predictions. [PERSON] et al. [5] reviewed renewable-energy prediction methods based on deep learning. The prediction methods were divided into four categories, namely deep belief networks, the stack auto-encoder, deep recurrent neural networks, and others. In addition, some data pre-processing and post-processing techniques were employed to improve forecasting accuracy. [PERSON] et al. [9] investigated artificial neural networks in forecasting energy and reliability. Energy sources in this study included solar, hydraulic, and wind. Many cases were provided to demonstrate the superiority of artificial neural  
networks in forecasting energy and reliability. [PERSON] et al. [10] surveyed applications and the taxonomy of machine-learning techniques in energy systems. Authors reported that hybrid models are superior to the traditional machine-learning models in applications of energy systems. [PERSON] and [PERSON] [11] investigated forecasting models of renewable power systems from aspects of power dispatching systems, energy storage systems, the energy policy and markets, reliability, and optimal reserve capacity. This review was useful to the power sector by providing recent trends and forecasting improvements of the system design and operations of power systems. [PERSON] et al. [7] reviewed applications of support-vector machine (SVM) models to forecasting solar and wind energy and indicated support-vector machine models outperformed the other forecasting models in terms of prediction accuracy. In addition, authors revealed that hybrid support-vector machine models can obtain better forecasting results than a single support-vector machine model. [PERSON] et al. [12] conducted a review and performance analysis of forecasting techniques in solar photovoltaic-power generations. This study indicated that the use of artificial neural networks and a support-vector machine model has been popular in this field. Authors pointed out that variations in weather conditions resulted in high forecasting errors in forecasting of the solar power. Because solar radiation is a main source of solar energy, [PERSON] et al. [13] performed a review of using machine-learning models in forecasting solar radiation. The authors reported that artificial neural networks, support-vector regression, regression tree, random forest, and gradient boosting are promising tools in solar radiation prediction, and hybrid models or ensemble forecast approaches are feasible ways to improve forecasting accuracy. [PERSON] et al. [14] reviewed classification algorithms for renewable energy problems and provided helpful insights for researchers as well as practitioners in this area. [PERSON] et al. [15] presented a comprehensive survey of hybrid renewable-energy systems. This study included hybrid renewable-energy system issues of feasibility analysis, optimum sizing, modeling, control and reliability, applications of evolutionary technique, and game theory. Table 1 depicts that the applications machine-learning techniques to renewable energy have been growing and shows that this issue is worth further exploring.