Season-Independent PV Disaggregation Using Multi-Scale Net Load Temporal Feature Extraction and Weather Factor Fusion

TL;DR

This paper introduces a novel PV disaggregation approach that combines hierarchical interpolation and multi-head self-attention to accurately separate PV generation from net load regardless of seasonal variations, enhancing energy system monitoring.

Contribution

It presents a new method integrating hierarchical interpolation and self-attention mechanisms for improved PV disaggregation from net load data.

Findings

Effective in real-world data simulations

Improves accuracy of PV generation predictions

Captures complex weather dependencies

Abstract

With the advancement of energy Internet and energy system integration, the increasing adoption of distributed photovoltaic (PV) systems presents new challenges on smart monitoring and measurement for utility companies, particularly in separating PV generation from net electricity load. Existing methods struggle with feature extraction from net load and capturing the relevance between weather factors. This paper proposes a PV disaggregation method that integrates Hierarchical Interpolation (HI) and multi-head self-attention mechanisms. By using HI to extract net load features and multi-head self-attention to capture the complex dependencies between weather factors, the method achieves precise PV generation predictions. Simulation experiments demonstrate the effectiveness of the proposed method in real-world data, supporting improved monitoring and management of distributed energy systems.