An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction

TL;DR

This paper introduces an LSTM-based power consumption prediction framework that uses asymmetric loss functions and anomaly detection to improve accuracy and reduce underpredictions, crucial for preventing power outages.

Contribution

It combines anomaly detection with asymmetric loss functions in LSTM models and applies seasonality splitting, enhancing load forecasting accuracy in residential power consumption.

Findings

Anomaly removal reduces prediction errors.

Asymmetric loss functions decrease underpredictions.

Seasonality splitting improves model performance.

Abstract

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.