A Novel Universal Solar Energy Predictor
This work addresses the need for continuous optimization of solar energy systems, which is incremental as it applies an existing machine learning method to a specific domain.
The paper tackled the problem of predicting solar energy generation by using a Naive Bayes classifier on historical weather and solar data, resulting in improved sensitivity and precision measures for photovoltaic energy prediction.
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.