NDLI: Generalized neural network methodology for short term solar power forecasting

Content Provider	IEEE Xplore Digital Library
Author	Singh, V.P. Vijay, V. Bhatt, M.S. Chaturvedi, D.K.
Copyright Year	2013
Description	Author affiliation: Electr. Dept., DEI Agra, Agra, India (Chaturvedi, D.K.) \|\| ERED Div., CPRI Bangalore, Bangalore, India (Bhatt, M.S.) \|\| CoE, Energy, IIT Jodhpur, Jodhpur, India (Singh, V.P.) \|\| CoE, Syst. Sci., IIT Jodhpur, Jodhpur, India (Vijay, V.)
Abstract	The main objective of this paper is to perform data analysis of ground based measurement and review the state of the art of IIT Jodhpur Rooftop solar photovoltaic installed 101 kW system. Solar power forecasting is playing a key role in solar PV park installation, operation and accurate solar power dispatchability as well as scheduling. Solar Power varies with time and geographical locations and meteorological conditions such as ambient temperature, wind velocity, solar radiation and module temperature. The location of Solar PV system is the main reason of solar power variability. Solar variability totally depends on system losses (deterministic losses) and weather parameter (stochastic losses). In the case of solar power, deterministic losses can be found out accurately but stochastic losses are very uncertain and unpredicted in nature. The proposed soft computing technique will be suitable for solar power forecasting modeling. In this paper Fuzzy theory, Adaptive Neuro-fuzzy interface system, artificial neural network and generalized neural network are used as powerful tool of solar power Forecasting. This soft computing cum nature inspired techniques are able to accurately and fast forecasting compared to conventional methods of forecasting. This is done analyzing the operational data of 101 kW PV systems (43.30 kW located in Block 1 and 58.08 kW in Block 2), during the year 2011.
Starting Page	58
Ending Page	62
File Size	1668336
Page Count	5
File Format	PDF
e-ISBN	9781479928033
DOI	10.1109/EEEIC-2.2013.6737883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-01
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training ANN Artificial neural networks Predictive models Solar power ANFIS Solar radiation Forecasting Biological neural networks Power forecasting Meteorology
Content Type	Text
Resource Type	Article

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