A Deep Learning Framework for Net Load Forecasting With Unsupervised Behind-the-Meter Disaggregated Data

Abstract

Recently, distributed photovoltaic (PV) generation has increased significantly, leading to a high penetration of behind-the-meter (BTM) solar generation systems. In this work, we aim to improve net load forecasting by disaggregating BTM components to provide better representation. For the disaggregation process, we propose an unsupervised contrastive-based optimization method for estimating BTM PV generation from the net load at the aggregated level. Our proposed method uses a deep neural network to leverage the strong correlation between solar irradiance and PV generation. This means that our proposed method is independent of the availability of BTM data and the assumption of a physical model. Furthermore, to obtain the best forecasted trends on the disaggregated series (pure load and PV generation), various recent forecasting models have been compared i.e. DeepAR, Temporal Fusion Transformer (TFT), and Time-series Dense Encoder (TiDE). The experiment is conducted on two real-world electricity prosumption datasets collected from New York and Texas. Results show that the net load forecasting on the disaggregated series outperforms the net load series directly. Such an improvement is due to the accuracy of our unsupervised disaggregation of the BTM data, proving superior to the semi-supervised technique.

Keywords

• Behind-the-meter
• net load
• PV generation
• net load disaggregation
• unsupervised learning