Research of Non-Intrusive Load Decomposition Considering Rooftop PV Based on IDPC-SHMM

Household electricity meters equipped with rooftop photovoltaic systems only display net load power data after coupling loads with photovoltaic power, which gives rise to the issue of unknown PV output and load demand. A non-invasive load decomposition algorithm based on Improved Density Peak Clustering (IDPC) and the Simplified Hidden Markov Model (SHMM) is proposed to decompose PV generation power and load consumption power from net load power data, providing data support for power demand-side management. First, the Improved Density Peak Clustering algorithm is used to adaptively obtain load power templates. Then, historical power data from PV proxy sites are classified based on weather types, while radiation proxies are used to estimate the historical PV power of the target users. These estimated PV power data are combined with historical load information to derive the parameters of the SHMM under different PV output conditions, thereby constructing the load decomposition objective function. Finally, the net load power data are used to achieve non-intrusive load decomposition and photovoltaic power extraction for households with PV systems; the effectiveness of the proposed algorithm is validated using Apmds datasets and Pecans Street datasets.