Improving economic operation of a microgrid through expert behaviors

Experimental results show that the method outperforms existing approaches in reducing operational costs, improving efficiency, and mitigating prediction uncertainty.

Optimization of Microgrid Dispatching by Integrating Photovoltaic

Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution

Frontiers | Ultra-short-term prediction of microgrid source load power

To enhance photovoltaic (PV) generation prediction accuracy, researchers have developed a forecasting algorithm based on LSTM (Hossain and Mahmood, 2020).

Advanced feature engineering in microgrid PV forecasting: A fast

Advanced Hybrid Model''s feature extraction and prediction outperform other models. This study introduces an innovative framework designed to forecast the fluctuating short-term generation

Forecasting renewable energy for microgrids using machine learning

This research explored the use of machine learning to forecast renewable energy generation and improve the operation of microgrids, which are small-scale power grids.

Artificial intelligence enabled microgrid power generation prediction

This article proposed machine learning-based short-term PV power generation forecasting techniques by using XGBoost, SARIMA, and long short-term memory network (LSTM) algorithms.

Enhancing Microgrid Performance Prediction with Attention

Our methodology underwent rigorous evaluation using the Micro-grid Tariff Assessment Tool dataset, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coeficient of

Machine learning-based energy management and power forecasting

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of

Microgrid power generation prediction method

This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of