The massive integration of variable renewable energy sources (RESs) poses the gradual necessity for new power system architectures with wide implementation of distributed battery energy storage systems (BESSs), which support power system stability, energy management, and control. This research presents a methodology and realization of a set of 11 BESS models based on different machine learning methods. The performance of the proposed models is tested using real-life BESS data, after which a comparative evaluation is presented. Based on the results achieved, a valuable discussion and conclusions about the models’ performance are made. This study compares the results of feedforward neural networks (FNNs), a homogeneous ensemble of FNNs, multiple linear regression, multiple linear regression with polynomial features, decision-tree-based models like XGBoost, CatBoost, and LightGBM, and heterogeneous ensembles of decision tree modes in the day-ahead forecasting of an existing real-life BESS in a PV power plant. A Bayesian hyperparameter search is proposed and implemented for all of the included models. Among the main objectives of this study is to propose hyperparameter optimization for the included models, research the optimal training period for the available data, and find the best model from the ones included in the study. Additional objectives are to compare the test results of heterogeneous and homogeneous ensembles, and grid search vs. Bayesian hyperparameter optimizations. Also, as part of the deep learning FNN analysis study, a customized early stopping function is introduced. The results show that the heterogeneous ensemble model with three decision trees and linear regression as main model achieves the highest average R² of 0.792 and the second-best nRMSE of 0.669% using a 30-day training period. CatBoost provides the best results, with an nRMSE of 0.662% for a 30-day training period, and offers competitive results for R²—0.772. This study underscores the significance of model selection and training period optimization for improving battery performance forecasting in energy management systems. The trained models or pipelines in this study could potentially serve as a foundation for transfer learning in future studies.

Accurate day-ahead solar irradiance forecasting is essential for reliable photovoltaic (PV) power generation and power system operation. This study proposes a machine-learning-based approach for site-specific day-ahead forecasting of plane-of-array (POA) irradiance using satellite-derived global horizontal irradiance (GHI) and meteorological predictors. Seven machine learning and deep learning models are evaluated using time-series data to forecast day-ahead POA irradiance from satellite-derived GHI. Training and evaluation are performed within a rolling-window validation framework, while hyperparameters are optimized using grid search and automated tuning. As baseline references, satellite-derived GHI is directly used as a proxy for site POA irradiance and compared with measured values, while a day-ahead persistence model is introduced as a simple benchmark. The experimental setup is designed to reflect an operational forecasting setting while relying on idealized meteorological inputs to isolate the modeling capability and assess the maximum achievable accuracy of day-ahead POA irradiance forecasting, which can be interpreted as an upper-bound performance scenario. The results show that machine learning models reduce the RMSE from 154.45 W/m² to 75.5 W/m² on the validation set, corresponding to an improvement of approximately 51% relative to the persistence baseline. Additionally, the impact of changepoint detection on the training process is investigated to account for structural shifts in the time series, and the influence of irradiance forecasting accuracy on photovoltaic power generation is evaluated through comparative PV energy yield calculations. The findings indicate that regression-based site adaptation of satellite-derived irradiance represents an effective approach for improving site-specific day-ahead POA irradiance forecasting while highlighting the importance of controlled evaluation conditions when assessing model performance.