Comparison of Adaptive Neuro-Fuzzy Inference Systems and Echo State Networks for PV Power Prediction

Use of photo-voltaic (PV) power as a source of electricity has been rapidly growing. Integration of large PV power into the grid operation introduces several challenges. Uncertainty of PV power generation causes frequency fluctuations and power system instabilities. Due to this, short term PV power prediction has become an important area of study. Short term PV power prediction supports proper decision-making in control centers. Power generation output of a PV plant is highly dependent on different weather conditions such as solar irradiance, temperature and cloud covers. Weather data analysis and prediction can be considered as big data due to its complexity and dynamically changing characteristics. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed and compared with an echo state network (ESN) for short term PV power prediction. The ANFIS approach consists of three ANFIS modules for predicting solar irradiance and temperature, and then estimating the PV power, respectively. The ESN on the other hand predicts the PV power based on current weather parameters. A weather database containing data sampled every second is used in developing the ANFIS and ESN based PV power prediction systems. Results are compared under different Clemson, SC weather conditions with the two approaches.