Microgrid load query website

Why is a microgrid more difficult to predict than a regional system?

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 . In addition, the drastic load fluctuation leads to low-precision prediction due to the limited load capacities in a microgrid.

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 .

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.

How accurate is short-term load Probability Density Forecasting in a microgrid?

However, related research of short-term load probability density forecasting is scarce in a microgrid. The prediction results accuracy varies substantially in microgrids with diverse capacities. In , a sparse heteroscedastic model is proposed to achieve the day-ahead probabilistic system-level load forecasting results.

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.

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.