Data-Driven Day-Ahead PV Estimation Using Hybrid Deep Learning

Publication Date: 9/23/2019

Event: 54th IEEE Industry Applications Society Annual Meeting, Baltimore, MD

Reference: PP 1-6, 2019

Authors: Yue Zhang, Washington State University, NEC Laboratories America, Inc.; Chenrui Jin, NEC Laboratories America, Inc.; Ratnesh K. Sharma, NEC Laboratories America, Inc.; Anurag K. Srivastava, Washington State University

Abstract: Ongoing smart grid activities and associated automation resulted in rich set of data. These data can be utilized for monitoring and estimation of real time photovoltaic (PV) generation. Inherent variability in PV and related impact on power systems is a challenging problem. Improving the accuracy of PV generation estimation is beneficial for both the PV owners and the grid operators. Recently, deep learning algorithms possible by the availability of data have shown its advantages for time series estimation; however, its application on PV generation estimation is still in the early stage. In this paper, a hybrid estimation model with a combination of long-short-term-memory network (LSTM) and persistence model (PM) is developed to provide day-ahead PV estimation at 15-minute time interval with high accuracy and robustness. Simulation results show the superior performance of the proposed method over existing methods for most of the test c

Publication Link: https://ieeexplore.ieee.org/document/8912017