An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction
This work addresses the challenge of preventing power outages in the residential sector by improving load forecasting accuracy, though it is incremental as it builds on existing LSTM and anomaly detection methods.
The paper tackled the problem of accurate power consumption forecasting with minimal underpredictions to prevent power outages, by proposing an LSTM framework with asymmetric loss functions and DBSCAN anomaly detection, which reduced underestimation errors across seasonal datasets from France, Germany, and Hungary.
Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density-based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, prior to the load forecasting task, to remove any present oultiers. Considering the effect of weather and social factors, seasonality splitting is performed on the three considered datasets from France, Germany, and Hungary containing hourly power consumption, weather, and calendar features. Root-mean-square error (RMSE) results show that removing the anomalies efficiently reduces the underestimation and overestimation errors in all the seasonal datasets. Additionally, asymmetric loss functions and seasonality splitting effectively minimize underestimations despite increasing the overestimation error to some degree. Reducing underpredictions of electricity consumption is essential to prevent power outages that can be damaging to the community.