Unsupervised anomaly detection and imputation in noisy time series data for enhancing load forecasting

Efficient energy management relies heavily on accurate load forecasting, particularly in the face of increasing energy demands and the imperative for sustainable operations. However, the presence of anomalies in historical data poses a significant challenge to the effectiveness of forecasting models, potentially leading to suboptimal resource allocation and decision-making. This paper presents an innovative unsupervised feature bank based framework for anomaly detection in time series data affected by anomalies. Leveraging an RNN-based recurrent denoising autoencoder, identified anomalies are replaced with plausible patterns. We evaluate the effectiveness of our methodology through a comprehensive study, comparing the performance of different forecasting models before and after the anomaly detection and imputation processes. Our results demonstrate the versatility and effectiveness of our approach across various energy applications for smart grids and smart buildings, highlighting its potential for widespread adoption in energy management systems.