Estimate and characterize PV power at demand-side hybrid system

Power forecasting, in a hybrid photovoltaic (PV) system, is an important issue regarding to the control and optimization of energy systems. In this work, multi-clustered echo state network (MCESN) models are proposed to directly perform the forecast of PV power generation. Furthermore, data characteristics of measured and estimated PV power are qualitatively investigated via data mining approaches. These characteristics include seasonality, stationarity (or non-stationarity) and complexity analysis. Simulation results indicate that the proposed MCESN model is able to precisely forecast PV power one-hour-ahead. The performance on the 24-h-ahead forecast is competitive with the correlation coefficient 99% for sunny days, and 91-98% for cloudy days. Results of data analysis unveil that critical characteristics between the measured and estimated PV power data are analogous. Comparison studies also show that MCESN could achieve more accurate prediction, compared with auto-regressive moving average (ARMA), back propagation (BP) neural networks.