What are these codes?

This is the offical implementation of the published paper. Please cite this work:

D. Kodaira, K. Tsukazaki, T. Kure, and J. Kondoh, "Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations, " Energies, vol. 14, no. 21, p. 7340, Nov. 2021.

Abstract

In this study, a two-step probabilistic forecast scheme is proposed for geographically distributed PV generation forecasting. Each step of the proposed scheme adopts ensemble forecasting based on three different machine-learning methods. When individual PV generation is forecasted, the proposed scheme utilizes surrounding PVs' past data to train the ensemble forecasting model.

Required packages

Matlab 2019

About the author

• Daisuke Kodaira, daisuke.kodaira03 AT gmail.com

Structure of the code

The proposed model is composed of two part; Singple PV foreacst model and Multiple PV foreacst model as shown above. Codes for the Single PV foreacaset model is Forecast_BasedOnSiglePVData.Codes for the Multiple PV foreacaset model is Forecast_BasedOnMultiplePVData. These codes work indivudually. The Single PV foreacst generate csv files which contains forecasted results. These csv files are utilized for Multiple OV foreacst model.

How to run

1. Singole PV foreacaset model

Run from "main.m".

2. Multiple PV foreacaset model

Run from "main.m".