This editorial overviews the contents of the Special Issue “Machine Learning for Energy Systems 2021” and review the trends in machine learning (ML) techniques for energy system (ES) optimization

Accurate power load forecasting is critical to maintaining the stability and efficiency of power systems. However, due to the complex and fluctuating nature of power load patterns, physical calculations are often inefficient and time‐consuming. In addition, traditional methods, known as statistical learning methods, require not only mathematical background and understanding but also statistical background and understanding. To overcome these difficulties, the authors proposed a simpler way to predict load by using artificial intelligence. This study investigated the performance of forecasting techniques, including three single‐layer and seven hybrid multilayer deep learning models that combine them. This study also analyzed the effect of hyperparameters on the learning results by varying the epoch and activation functions. To evaluate and analyze the performance of the deep learning model, this study used load data from the power system in Jeju Island, Korea. In addition, this study included weather factors that may affect the load to improve the prediction performance. The prediction process is performed on the Python platform, and the model that showed the highest accuracy was LSTM‐CNN, a hybrid combination of LSTM and CNN models. Considering both the maximum and minimum error, the error value was low at 0.231%. By providing detailed insights into the entire research process, including data collection, preprocessing, scaling, prediction, and analysis, this study provided valuable guidance for future research in this area.

The power requirements of grids have risen as artificial intelligence and electric vehicle technologies have been used. Thus, the installation of distributed generators (DGs) has become an essential factor to streamline power grids. The objective of this study is to optimize the capacity and location of DGs. For this purpose, an objective function was defined, which takes into account the fault current and the levelized cost of energy, and a modified particle swarm optimization method was applied. Then, we analyzed a case of a single line-to-ground fault with a test feeder (i.e., the IEEE 30 bus system) with no DGs connected, as well as a case where the DGs are optimally connected. The effect of the optimally allocated DGs on the system was analyzed. We discuss an optimal layout method that takes the economic efficiency of the DG installation into account.