<![CDATA[Solar Energy is established as an alternative source of energy known as renewable energy. In a developing country like India, the perspective of Solar Energy is important, as it supports a limitless source of energy. Monitoring and prediction of photo-voltaic energy generation help to reduce the energy loss and empower to utilize more energy. Solar energy prediction is challenging as it depends on the fluctuating solar radiations and climate conditions. The problem statement is to monitor solar panels and predict energy generation for energy management procedure. In this paper, the Internet of Things and Machine Learning algorithms are used as a powerful tool for developing a smart solar system. The metro-logical data such as humidity, temperature and photovoltaic panel data is used as input to forecast solar power generation. For prediction, we examine time-series of solar energy data with Hidden Markov Model. This model considers the probabilistic correlation between previous values to next value in time-series. Experimental results shows that individual panel dead state is located successfully and time-series based solar energy prediction emulate the actual power generation.]]>
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