17-03-2014, 07:09 PM
Abstract
Efficiency and certainty of payback have not yet
attained desired level for solar photovoltaic energy
systems. Despite huge development in prediction of
solar radiation data, a clear disconnect in extraction
and effective engineering utilization of pertinent
information from such data is acting as a major
roadblock towards penetration of this emerging
technology. It is crucial to identify and optimize the
most significant statistics representing insolation
availability by a solar PV installation for all necessary
engineering and financial calculation. A MATLAB
program has been used to build the annual frequency
distribution of hourly insolation over any module plane
at a given site location. Descriptive statistical analysis
of such distributions is done through MINITAB. To
make the analysis more meaningful, composite
frequency distribution for a Building Integrated Photo
Voltaic (BIPV) set up has been considered, which is
formed by weighted summation of insolation
distributions for different module planes used in the
installation. The most influential statistics of the
composite distribution have been optimized through
Artificial Neural Network Computation. This novel
approach is expected to be a very powerful tool for the
BIPV system designers.
1. Introduction
In recent years quality engineering gap has been
figured out as a major factor behind the performance
uncertainty of solar photovoltaic energy systems. In
fact this techno-management shortcoming is to some
extent responsible for the inability of solar PV
technology to attract adequate investment & business
intelligence required for its desired market penetration.
Quality engineering tools and techniques, which are in
many cases based on proven statistical methods, can
effectively bridge the critical gap between laboratory
and life for any emerging technology. Earlier the
authors came out with a novel algorithm based on
Statistical Methods and Quality Engineering tools like
Designed Experiment, ANOVA, Regression modeling
and Response Surface Methodology for accurate
prediction of PV module behavior under any given
environmental condition [1]. A deeper look into the
term “given environmental condition” revealed the
necessity of objective quantification of the insolation
availability for a site and more precisely for the solar
photovoltaic installation at that site. Keeping in view
that the ‘insolation availability at a site’ and ‘insolation
availability by a Solar PV installation’ are not
synonymous, the discussion in the subsequent sections
will reveal how lack of clarity in understanding and
estimating the most appropriate ‘Statistic(s)’
representing the insolation availability and its variation
may lead to wrong selection of PV modules, over
specification and cost escalation vis-à-vis error in
performance prediction of the solar PV energy
systems. In this paper the authors have come out with a
new way of defining the ‘insolation availability’ on a
solar PV installation at a project site following the
three steps given below:
Efficiency and certainty of payback have not yet
attained desired level for solar photovoltaic energy
systems. Despite huge development in prediction of
solar radiation data, a clear disconnect in extraction
and effective engineering utilization of pertinent
information from such data is acting as a major
roadblock towards penetration of this emerging
technology. It is crucial to identify and optimize the
most significant statistics representing insolation
availability by a solar PV installation for all necessary
engineering and financial calculation. A MATLAB
program has been used to build the annual frequency
distribution of hourly insolation over any module plane
at a given site location. Descriptive statistical analysis
of such distributions is done through MINITAB. To
make the analysis more meaningful, composite
frequency distribution for a Building Integrated Photo
Voltaic (BIPV) set up has been considered, which is
formed by weighted summation of insolation
distributions for different module planes used in the
installation. The most influential statistics of the
composite distribution have been optimized through
Artificial Neural Network Computation. This novel
approach is expected to be a very powerful tool for the
BIPV system designers.
1. Introduction
In recent years quality engineering gap has been
figured out as a major factor behind the performance
uncertainty of solar photovoltaic energy systems. In
fact this techno-management shortcoming is to some
extent responsible for the inability of solar PV
technology to attract adequate investment & business
intelligence required for its desired market penetration.
Quality engineering tools and techniques, which are in
many cases based on proven statistical methods, can
effectively bridge the critical gap between laboratory
and life for any emerging technology. Earlier the
authors came out with a novel algorithm based on
Statistical Methods and Quality Engineering tools like
Designed Experiment, ANOVA, Regression modeling
and Response Surface Methodology for accurate
prediction of PV module behavior under any given
environmental condition [1]. A deeper look into the
term “given environmental condition” revealed the
necessity of objective quantification of the insolation
availability for a site and more precisely for the solar
photovoltaic installation at that site. Keeping in view
that the ‘insolation availability at a site’ and ‘insolation
availability by a Solar PV installation’ are not
synonymous, the discussion in the subsequent sections
will reveal how lack of clarity in understanding and
estimating the most appropriate ‘Statistic(s)’
representing the insolation availability and its variation
may lead to wrong selection of PV modules, over
specification and cost escalation vis-à-vis error in
performance prediction of the solar PV energy
systems. In this paper the authors have come out with a
new way of defining the ‘insolation availability’ on a
solar PV installation at a project site following the
three steps given below: