A real-time electrical load forecasting and unsupervised anomaly detection framework

We propose a unified machine learning (ML) framework for simultaneously performing electrical load forecasting and unsupervised anomaly detection in real time. For load forecasting, we introduce a training data generator (TDG) and a look-back optimizer (LBO) to enhance the performance of a baseline ML-driven prediction approach. The proposed TDG selects a subset of the entire historical dataset of power consumption to derive a relatively small training dataset that is used for forecasting using ML. It is designed to operate on raw meter data, without requiring any input data conditioning or additional information (e.g., weather, building occupancy), thus providing a data-efficient training approach. The proposed LBO optimizes prediction performance by tracking the accuracy of prior forecasts and automatically adjusting the framework to improve prediction outcomes. For the problem of anomaly detection, the proposed framework is unsupervised and determines abnormality by comparing fluctuations in the current load with power consumption changes in the training dataset. Therefore, in addition to not requiring any data-labeling, it circumvents the problem of imbalanced classes associated with ML-based anomaly detection methods. The proposed framework is evaluated using a real-world power consumption dataset from a large US-based university campus with considerable power usage complexity. Twelve different evaluation metrics are used to comprehensively assess the effectiveness of our approach. Our evaluation results show that compared with alternative methods, the proposed framework consistently achieves superior outcomes with regard to both load forecasting and anomaly detection, representing a promising approach to electrical load forecasting and anomaly detection in practice.