SHORT-TERM POWER FORECASTING BY STATISTICAL METHODS FOR PHOTOVOLTAIC PLANTS IN SOUTH ITALY

• Abstract:

  Statistical methods based on Multiregression Analysis and Artificial Neural Networks (ANNs) have been developed in order to predict power production of a 960 kWp grid-connected photovoltaic (PV) plant in the campus of the University of Salento, Italy.
  The neural network has been used only as a statistic model based on time series of PV power and meteorological variables, as module temperature, ambient temperature and irradiance on module’s plain. In particular, a sensitivity analysis has been carried out in order to find those weather parameters with the best impact on the forecasting.
• Keywords:

  forecasting, photovoltaic power, Artificial Neural Networks, prediction, Multiregression Analysis
• Download:

  IMEKO-TC19-2013-034.pdf
• DOI:

  _unreg_tc19-2013.034

Event details:

• IMEKO TC:

  TC19
• Event name:

  Protecting Environment, Climate Changes and Pollution Control
• Title:

  4th Symposium on Environmental Instrumentation and Measurements
• Place:

  Lecce, ITALY
• Time:

  3 June 2013 - 4 June 2013