Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it helps reduce, generate, and operate costs by balancing supply and demand. Recently, the challenge in STLF has been the load variation that occurs in each period, day, and seasonality. This work proposes the bagging ensemble combining two machine learning (ML) models—linear regression (LR) and support vector regression (SVR). For comparative analysis, the performance of the proposed model is evaluated and compared with three advanced deep learning (DL) models, namely, the deep neural network (DNN), long short-term memory (LSTM), and hybrid convolutional neural network (CNN)+LSTM models. These models are trained and tested on the data collected from the Electricity Generating Authority of Thailand (EGAT) with four different input features. The forecasting performance is measured considering mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) parameters. Using several input features, experimental results show that the integrated model provides better accuracy than others. Therefore, it can be revealed that our approach could improve accuracy using different data in different forecasting fields.

Battery energy storage systems and multilevel converters are the most essential constituents of modern medium voltage networks. In this regard, the modular multilevel converter offers numerous advantages over other multilevel converters. The key feature of modular multilevel converter is its capability to integrate small battery packs in a split manner, given the opportunity to submodules to operate at considerably low voltages. In this paper, we focus on study of potential SMs for modular multilevel converter based battery energy storage system while, keeping in view the inconsistency of secondary batteries. Although, selecting a submodule for modular multilevel converter based battery energy storage system, the state of charge control complexity is a key concern, which increases as the voltage levels increase. This study suggests that the half-bridge, clamped single, and full-bridge submodules are the most suitable submodules for modular multilevel converter based battery energy storage system since, they provide simplest state of charge control due to integration of one battery pack along with other advantages among all 24 submodule topologies. Depending on submodules analysis, the modular multilevel converter based battery energy storage system based on half-bridge submodules is investigated by splitting it into AC and DC equivalent circuits to acquire the AC and DC side power controls along with an state of charge control. Subsequently, to validate different control modes, a downscaled laboratory prototype has been developed.