A real-time carbon emission estimation framework for industrial parks with non-intrusive load monitoring

Abstract

Accurate carbon emission accounting is critical for combating climate change. Real-time estimation of emissions can further facilitate precise carbon emission control, emission reduction planning, and post-analysis. Industrial parks are significant carbon emitters and require more attention to their carbon emissions and environmental impact. However, current carbon emission estimation methods for industrial parks have limitations in terms of time lag, inconsistent standards, and low reliability. In response, an innovative real-time carbon emission estimation framework for industrial parks is proposed to improve the estimation accuracy while taking into account both Scope 1 and Scope 2 emissions. It is a data-driven method based on non-intrusive load monitoring (NILM) algorithms and reliable real-time meter data. It estimates the emissions of the factories based on factory-level data and then aggregates it. Scope 1 emissions are calculated based on the direct carbon emission factors of the devices (DEFs) and the corresponding device states obtained by a novel NILM algorithm named Adaptive Weighted Recurrence Graph-Convolutional Neural Network-Bidirectional Long Short-Term Memory (AWRG-CNN-BLSTM), achieving an average identification accuracy of 93.4%. Scope 2 emissions from imported electricity or heat are calculated using more accurate electricity-related or heat-related emission factors, locational marginal carbon emission factors of electricity (MEFs), and locational carbon emission factors of heat consumption (LEFs), respectively. The experiments on an industrial park with four factories demonstrate the effectiveness of our proposed method. The carbon emission estimation error is only 0.44% in one year, which is 2.32% lower than that of the IPCC method. Moreover, real-time estimation enables emissions tracking.