Mr. Matsumoto’s paper about photovoltaic output power forcasting under smartgrid environment was published in a journal of IEICE.
Title: High Density Short-Term Forcasting Photovoltaic Output Power Architecture without Meteorological Observations in Smart Grid
Authors: Jun Matsumoto, Daisuke Ishii, Satoru Okamoto, Eiji Oki, and Naoaki Yamanaka
Abstract: We propose a forecasting architecture of near future photovoltaic output power based on the multipoint output power data via smart meter. The conventional forecasting methods are based on the analysis of meteorological observation data, and need the implementation of dedicated meters and the connection to them. Moreover, short-term forecasting is difficult in the conventional methods, since the short-term output power variation is irregular. Our proposed method is based on not meteorological observation data but the actual measured output power data by using the solar panels connected with a smart meter as sensing units. A forecasting calculation server interpolate spatially the actual measured data collected from multipoint, and forecasts near future output power in each point using optical flow estimation. Virtual sampling technique involves the forecast performance when the sampling point is sparse. We show effectiveness of the proposed forecasting method and improvement in forecasting accuracy with virtual sampling by the computer simulation.