# Special Issue on Applications of Artificial Neural Networks for Energy Systems
## 2 Artificial Neural Networks for Energy Systems  
In light of the above, this special issue was introduced to collect latest research on relevant topics, and more importantly, to address present challenging issues with the applications of Artificial Neural Networks (ANNs) for energy systems, with specific attention to the power systems. There were 38 papers submitted to this special issue, and 12 papers were accepted (i.e., a 29% acceptance rate). Various topics have been addressed in this special issue, mainly on non-programmable renewables (e.g., photovoltaic, wind and wave technologies) but also on batteries, fuel cells, biofuel and, the Internet of energy. The first two papers regard the application of ANNs to transmission systems [2] and weak connected or stand-alone distribution systems [3]. Specifically paper [2] is about the reduction of occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances which are mainly caused byrenewables. A preventive strategy for the automatic generation control (AGC) of power systems is proposed, but to mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, a deep forest reinforcement learning (DFRL) method is proposed as a preventive strategy for AGC. Paper [3] is about the design of a power management system for a stand-alone rural microgrid that is able to forecast both the local load demand and the generation from renewable sources in order to control the fossil fuel-based generators [3]. To address this problem, a hybrid machine learning algorithm is proposed. It divides the task of power load prediction into two sequential sub-models: the first model predicts the future weather conditions and the second model is trained to associate the predicted weather conditions with the load demand. In this special issue, to confirm the crucial role of PVs in development of future power systems, there are three papers focused on photovoltaic (PV) systems, and forecasting as the main problem faced for best use of these systems. The first paper [4], is about the use of ANNs to estimate and forecast solar radiation. Global horizontal irradiation (GHI) data are rarely measured worldwide, thus an artificial neural network (ANN) model is built to calculate this data based on available data. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. A second ANN model was developed to transform GHI into global tilted irradiation (GTI). The GTI calculation from GHI was realized with an nRMSE of around 8% for the optimal configuration. PV power generation widely varies due to environmental factors, meaning the accurate forecasting of PV generation is essential. To achieve this, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for time horizons varying from one hour (h + 1) to six hours (h + 6). The forecasting of hourly solar irradiation from h + 1 to h + 6 using ANN was realized with an nRMSE from 22.57% for h + 1 to 34.85% for h + 6 for GHI, and from 38.23% for h + 1 to 61.88% for h + 6 for DNI. In the second paper [5], an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural networks is proposed. The applied solution used improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data is collected and used. Missing input values reduce the PV power generation prediction performance. In reference [6], the impact of missing data imputation methods that can be used to replace these missing values is explored. We applied four missing data imputation methods to the training data and test data of the prediction model based on support vector regression.