Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
In recent years focus has been on environmental pollution issue resulting from consumption of fossil fuels, e.g., coal and oil. Thus introduction of an alternative energy source such as solar Photo Voltaic (PV) energy is gaining momentum. Short-term photovoltaic power generation forecasting is an important task in renewable energy power system planning and operation. Based on seasonal weather classification, the Back Propagation (BP) Artificial Neural Network (ANN) approach is utilized to forecast the next 24 h PV power outputs, using weather database which include global irradiance, temperature, wind speed and humidity data of Chennai city (South-East coast of India) using a data acquisition system. The estimated results of the proposed PV power forecasting model coincide well with measurement data for a 10 kW roof top grid connected PV system. The future DC and AC power outputs are predicted for any given day. The proposed approach achieves better prediction accuracy for hot and humid climatic region.
How to cite this article:
Ashwin Kumar Sahoo. Energy Forecasting for Grid Connected Solar PV System Based on Weather
Classification.
DOI: https://doi.org/10.36478/ajit.2016.4861.4874
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2016.4861.4874