Microgrid load power prediction method

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 [, , , ].

How can clustering and probability load forecasting be used in microgrids?

A combination of the clustering method and probability load forecast method can potentially be used to reduce the load forecasting error in a microgrid and for analyzing the relationship between forecasting accuracy with load characteristics.

Is microgrid load forecasting a stochastic model?

By contrast, a stochastic model for microgrid load forecasting is proposed in , but the load features are not taken into account in the constructed model. Therefore, due to its smaller capacity, higher volatility, and higher randomness, the microgrid load is more challenging to forecast than in a large power grid.

Can ml improve load demand forecasting accuracy in microgrids?

According to Table 5, the studies reveal that ML techniques hold the potential to improve load demand forecasting accuracy in microgrids by addressing uncertainties and energy consumption patterns. ML techniques combine different algorithms to create more robust and adaptable load demand prediction models.

Can deterministic load forecasting predict controllable load in a microgrid?

However, deterministic load forecasting cannot reveal the load pattern and uncertainty of controllable load in a microgrid, where the prediction errors may exceed the expected range due to the high volatility and strong randomness.

Why is microgrid load more difficult to forecast?

These essential methods have been widely applied in system-level load forecasting applications and achieved accurate prediction results. Nevertheless, the microgrid load is more difficult to forecast than a regional system due to the high randomness and lower similarities in its historical load curves .