ECTI TRANSACTIONS ON COMPUTER INFORMATION TECHNOLOGYVolume 13, No. 02, Month NOVEMBER, Year 2019, Pages 188 - 195
Very short-term photovoltaic power forecasting using stochastic factors
Kriangkamon Khumma, Kreangsak Tamee
Abstract Download PDF This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm ﬁltering technique to estimate power output and the creation of a stochastic forecasting model. As a result, aﬀected power can be forecasted from stochastic factors with machine learning and an artiﬁcial neural network. This model focuses on very short-term forecasting over a ﬁve minute period. As it uses only endogenous data, no exogenous data is needed.
To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. This proposed PV forecasting model gained higher accuracy than the persistence model using stochastic factors.
Neural Network, Solar PV Generation Power Forecast, Stochastic Factor