• Nirupam Bidikar
• Kotoju Rajitha
• P. Usha Supriya

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 Naïve 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 implementing this approach, we observe a noticeable improvement in the accuracy and sensitivity and also explore the how photovoltaic energy generation is affected by various solar parameters.

Solar energy, Photovoltaic Energy, Machine Learning, Naive Bayes‘ classifier

"A NOVEL UNIVERSAL PHOTOVOLTAIC ENERGY PREDICTOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.334-344, February 2019, Available :http://www.jetir.org/papers/JETIR1902751.pdf

"A NOVEL UNIVERSAL PHOTOVOLTAIC ENERGY PREDICTOR", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp334-344, February 2019, Available at : http://www.jetir.org/papers/JETIR1902751.pdf