A Two-Tier Analysis Method for Adjustable Potential of Building Electricity Use Based on Electricity Consumption Data

With the rapid development of new power systems, it is urgent to deeply explore the characteristics of building load ontology and the influence of electricity consumption behavior factors on the adjustable potential. Based on the collected electricity consumption data, this paper proposes a two-layer analysis method for the potential of electricity consumption adjustment. Firstly, the density clustering algorithm (DBSCAN) was used to classify the load ontology characteristics by three key indicators: load rate, daily peak-to-valley difference and peak-to-valley difference rate. Secondly, principal component analysis (PCA) was used to reduce the dimensionality of building electricity consumption behavior factors to improve the speed and accuracy of clustering, and then the degree of dispersion is clustered by Fuzzy C-Means (FCM) to analyze the user's adjustable potential. Finally, the effectiveness and practicability of the DBSCAN-PCA-FCM two-layer method proposed in this paper are verified by using the measured building electricity consumption data and the influencing factors of building electricity consumption behavior in eastern China, which can provide a theoretical basis for users to participate in demand-side response.