# Special Issue on Applications of Artificial Neural Networks for Energy Systems
## 2 Artificial Neural Networks for Energy Systems
When the k-nearest neighbour's method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model's performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method. The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines into electrical grids, two papers about this problem are present in this special issue. Specifically, in reference [7] a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model is proposed to improve forecasting accuracy. It is divided in three steps: (1) the original wind speed series is decomposed into a series of components by using EEMD; (2) the FuzzyEn is used to calculate the complexity of each component, and the components with similar FuzzyEn values are classified into one group; (3) the LSTMNN model is used to forecast each subsequence after classification. The forecasting result of the original wind speed series is obtained by aggregating the forecasting result of each subsequent event. The development of a short-term wind power forecasting model based on ANN clustering, which uses statistical feature parameters in the input vector, while an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method is proposed in [8]. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. Energy harvested from a Wave Energy Converter (WEC) varies greatly with the location of its installation. Determining an optimal location that can result in maximum output power is therefore critical. In reference [9], a novel approach to predicting the output power of a near shore WEC by characterizing ocean waves using floating buoys is presented. Experimental data are utilized to train and test prediction models. The models were developed using machine learning algorithms: SVM, RF, and ANN. The results of the experiments shows that measurements from the data collection module can yield a reliable predictor of output power. Furthermore, it found that the predictors work better when the regressors are combined with a classifier. The proposed prediction model could be used in both locating optimal placement for WECs and designing the shape of the buoys used by them. Solid Oxide Fuel Cells (SOFCs) are promising candidates to coach the energy transition thanks to their fuel flexibility. However, SOFC performance are markedly affected by fuel composition and operative parameters. In order to optimize SOFC operation and to provide a prompt regulation, reliable performance simulation tools are required. Given the high variability ascribed to the fuel in the wide range of SOFC applications and the high non-linearity of electrochemical systems, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is sound. In reference [10], several network architectures based on a feedforward-backpropagation algorithm are proposed and trained on experimental data-set issued from tests on commercial NiYSZ/8 YSZ/LSCF anode supported planar button cells. Remaining useful life (RUL) is the useful life left on an asset at a particular time of operation. Its estimation is central to condition based maintenance and prognostics and health management. RUL is typically random and unknown, and as such it must be estimated from available sources of information such as the information obtained from conditions and health monitoring. On-line RUL prognosis is a crucial problem for aerospace Lithium-ion (Li-ion) batteries.