Microgrid power load forecast table

How accurate is load forecasting in power microgrids?

An accurate method with acceptable training time using load and meteorological data. Load forecasting in power microgrids and load management systems is still a challenge and needs an accurate method. Although in recent years, short-term load forecasting is done by statistical or learning algorithms.

Why is load forecasting important for microgrid energy management?

Accurate forecasting of load and renewable energy is crucial for microgrid energy management, as it enables operators to optimize energy generation and consumption, reduce costs, and enhance energy efficiency. Load forecasting and renewable energy forecasting are therefore key components of microgrid energy management [, , , ].

Why is forecasting important in microgrids?

Furthermore, medium-term forecasting is important for the mid-term planning of the system. Long-term forecasting is particularly important for load forecasting as it affects network development. There are various techniques for forecasting in microgrids, but ANN, ML, and DL are the most important of them.

How is STLF forecasting used in advanced microgrid (MG) applications?

The precise modelling and complex analyses of STLF have become more significant in advanced microgrid (MG) applications. Several models are proposed for STLF and tested successfully in the literature. The selection of a forecasting method is mostly based on data availability and its objectives.

Does solar power integration affect microgrid load forecasting?

To evaluate the impact of solar power integration in microgrid load forecasting, the net and total load are predicted and compared for two real microgrid case studies. The assessment has been done using various statistical error metrics.

How accurate is solar energy forecasting for microgrids?

The paper highlights the significance of accurate solar energy forecasting for microgrids by comparing AI techniques and showing that DL algorithms outperform ML algorithms in providing more accurate predictions. This research contributes to the effective load management and integration of clean energy.