# A Survey of Machine Learning Models in Renewable Energy Predictions
## 2 Machine-Learning Models in Renewable-Energy Predictions
### The Status of Machine-Learning Technology Used in Renewable-Energy Forecasting
[105] investigated heat exchangers of geothermal installations with the goal of using previous data to obtain accurate predictions of the sensors placed along the heat exchangers in systems. The results showed that the time-dependent neural networks (TDNN) model is superior to other standard regression indicators in all cases. Bayesian linear regression, neural network regression, boosted decision-tree regression, and decision forest regression were used to predict the water levels of a reservoir, which is critical to hydropower generation [106]. The study revealed that Bayesian linear regression can obtain superior forecasting results than the other three forecasting models in terms of forecasting accuracy. [107] designed a forecasting system (LMS-BSDP) by employing inflow predictions with various lead-times intervals to increase the performance of hydropower stations in hydropower predictions as well as reliability. Based on the proposed forecasting model with accurate forecasting performance, proper operation policies of hydropower stations can be provided. A hybrid model (PSO-SVM) based on support-vector machines and particle swarm optimization (PSO) was presented to forecast the higher heating value (HHV) in a roasting process of the biomass-energy generation [108]. The particle swarm optimization was used to determine parameters of support-vector machines. Experiment results indicated that the proposed PSO-SVM model with cubic kernel functions can generate more accurate forecasting HHV results than the other forecasting models. Fuzzy inference systems (FIS) and artificial neural networks (ANN) were proposed to predict wave energy in faster and cheaper ways [109]. This study reported that that the proposed forecasting models is able to predict wave power effectively and efficiently at any place in deep oceanic waters. [110] used the Gaussian process (GP) to forecast short-term waves, which is essential to wave energy converters. Two other forecasting models, neural network (NN) and autoregressive modeling (AR), were employed to deal with the same dataset to demonstrate the performance of the GP models. The numerical results illustrated that the GP model outperformed the other two models. Furthermore, the GP model is capable of dealing with the uncertainty of short-term waves. A fuzzy inference system using interval type -2 fuzzy inference system 2 (IT2 FIS) was designed to forecast waves [111]. The proposed IT2 FIS used meta-cognitive learning algorithms to capture knowledge from wave data. This study revealed that the proposed ITSFIS is a promising alternative in-wave forecasting. [112] Multiple regression and artificial neural networks were employed to predict drilling parameters of rates of penetration (ROP) while generating geothermal energy. The investigation showed that both models can provide high values of correlation and, therefore, improve prediction performance of ROP predictions. [113] used random-forest algorithms and data collected from geographic information systems to predict very shallow geothermal opportunity. The study indicated that the random forest is a feasible and promising way to forecast geothermal energy when data, such as topography, weather and soil, are available. Table 2 illustrates that wind and solar are the two renewable energies often investigated, and artificial intelligence and hybrid models are the two frequently employed techniques. _Appl. Sci._**2020**, _10_, 5975