  Solar energy is one of the most economical and clean sustainable energy
sources on the planet. However, the solar energy throughput is highly
unpredictable due to its dependency on a plethora of conditions including
weather, seasons, and other ecological/environmental conditions. Thus, the
solar energy prediction is an inevitable necessity to optimize solar energy and
also to improve the efficiency of solar energy systems. Conventionally, the
optimization of the solar energy is undertaken by subject matter experts using
their domain knowledge; although it is impractical for even the experts to tune
the solar systems on a continuous basis. We strongly believe that the power of
machine learning can be harnessed to better optimize the solar energy
production by learning the correlation between various conditions and solar
energy production from historical data which is typically readily available.
For this use, this paper predicts the daily total energy generation of an
installed solar program using the Naive Bayes classifier. In the forecast
procedure, one year historical dataset including daily moderate temperatures,
daily total sunshine duration, daily total global solar radiation and daily
total photovoltaic energy generation parameters are used as the categorical
valued features. By way of this Naive Bayes program the sensitivity and the
precision measures are improved for the photovoltaic energy prediction and also
the consequences of other solar characteristics on the solar energy production
have been assessed.
