Ultra-short-term forecasting for photovoltaic power plants and real-time key performance indicators analysis with big data solutions. Two case studies - PV Agigea and PV Giurgiu located in Romania

Nowadays, plenty of data is continuously pouring from the PhotoVoltaic Power Plants (PV) monitoring systems and sensors that could be successfully handled by big data technologies. This paper proposes a methodology that automatically collects the data logs from sensors installed on PV arrays, inverters and weather stations, checks the health status of the PV components, forecasts the generated power for each inverter based on its real operating conditions and the predicted irradiance and finally provides use fulinsights of the PV system based on the Key Performance Indicators (KPI) using big data technologies. The Ultra-Short-Term Forecast (USTF) algorithm provides the estimations of irradiance and generated power for the next 30 min and is applied on a sliding time window interval. The algorithm uses a Feed-Forward Artificial Neural Network (FF-ANN) and, to significantly reduce the number of iterations, we propose a backtracking adjustment of the learning rate that enables faster convergence reducing the computational time that is essential for USTF. Two data sets from PV A gigea 0.5 MW and PVG iurgiu 7.5 MW, located in the South-East and South of Romania, that consist in data logs from inverters and arrays, are used for simulation. The exhaustive analyses are performed for PV Agigea (including KPI calculation), while PV Giurgiu data set was mainly used to check the scalability and replicability of the algorithm. (C) 2020 Elsevier B.V. All rights reserved.