Abstract:
Today, for the management of energy supply systems forecast information
on load and the production of meteorology dependent (wind, solar, hydro)
generation is ever rising. Solar irradiance forecasting is given a unique
priority as it spans over major applications such as management of grids
with a high share of photovoltaic generation and thermal power supply
systems relying on solar heat generation. This research addresses the day
ahead prediction of the local irradiances intended to be applied for the
management of solar assistant systems for heat and hot water supply. The
forecast method presented here is based on the statistical analysis of
historical data in Kristiansand, Southern Norway. For this, satellite derived
irradiance data covering seven years provided by Geomodel Solar,
Slovacia (D. Heinemann, 2005) can be used. In this approach, it is assumed
that the irradiance sum of today shows a dependence on the irradiance sum
of yesterday (B.O. Ngoko, 2014). This day to day dependency is assessed
by obtaining conditional probability distributions of irradiance sum on
next day for a given status of weather, given here by the irradiance sum on
previous day. Based on such probabilistic approach two schemes are
introduced to obtain values for the forecasting. The first scheme is based
on most probable expected irradiance sum of tomorrow and the second
approach is based on the average expected irradiance sum, both extracted
from the probability distributions. Having obtained forecasted values for
the irradiance, the validity of prediction methods are investigated by
comparing with the actual measured data giving the statistical parameters,
relative monthly Bias and relative monthly Root Mean Square Error
(RMSE). The comparison reveals that the approach using the average
expected irradiance sum, gives more accurate results showing low RMSE.
Concerning the application, the irradiance data, both measured and
forecasted can be used to analyze the daily energy gain of a solar thermal
collector and its forecastability.
