Solar Energy Prediction Model using machine learning and Internet of Things

There is been a huge shortage in the primary source of electricity all over the world and in upcoming years it is going to be vanished. One of the most worrying problems today is rising energy prices. Energy costs are rising as the Earth's resources are gradually depleted. Fortunately, technology has provided new resources from natural entities such as solar energy. Although the demand for energy continues to rise, there are things that every homeowner can do to reduce their costs and help the environment. One of the most abundant, economic and pollution free source of power is the solar energy which can be used as the power source if we collect and use the proper information of the solar power. Proposed prediction model uses the data available over the different areas and provide the wholesome information about the solar energy like Intensity, timings, weather condition effects. Different algorithms of Machine Learning (ML) like regression are been used for the predictions and calculations of the relevant data and for the household consumption stats the Internet of Things (IoT) modules are being used. Different areas households' consumption stats will be monitored and compared to draw the maximum power outputs from the solar energy so that multiple appliances can be used. The designed prototype is validated with several test cases and comparison of the performance is carried with state of art prediction schemes.