A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites

作者：Shaker Hamid^*; Zareipour Hamidreza; Wood David

来源：IEEE Transactions on Smart Grid, 2016, 7(5): 2466-2476.

DOI：10.1109/TSG.2015.2502140

• Big data
• behind-the-meter solar
• clustering
• data dimension reduction
• invisible solar power generation
• principal component analysis

Roof-top solar photovoltaic systems are normally invisible to system operators, meaning that their generated power is not monitored. If a significant number of systems are installed, invisible solar power could significantly alter the net load in power systems. In this paper, a data-driven methodology is proposed to estimate the power generation of invisible solar power sites by using the measured values from a small number of representative sites. The proposed methodology is composed of a data dimension reduction engine and a mapping function. A number of established methods for reducing the dimension of large-scale data is investigated, and a hybrid method based on k-means clustering and principal component analysis is proposed. The output of this block provides a small subset of sites whose measured data are used in the mapping function. We have implemented several mapping functions to estimate the total generation power of all sites based on the measured output of the selected subset of sites. Numerical results based on data from California's power system are presented.

• 出版日期2016-9