Skip to main content
Fig. 6 | Energy Informatics

Fig. 6

From: Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data

Fig. 6

Average feature importances for smart meter-based LSTM (LSTM SM). We can see that the aggregated energy community load, the sin and cos transform of the hour and day, weekend binary as well as selected households serve as most important input features to forecast day-ahead energy community loads. Interestingly, most important households are also amongst the households with the highest annual electricity consumption

Back to article page