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Climate change and the energy crisis substantially motivated the use and development of renewable energy resources. Solar power generation is being identified as the most promising and abundant source for bulk power generation. However, solar photovoltaic panel is heavily dependent on meteorological data of the installation site and weather fluctuations. To overcome these issues, collecting performance data at the remotely installed photovoltaic panel and predicting future power generation is important. The key objective of this paper is to develop a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention is not possible due to harsh weather conditions or other circumstances. An Internet of Things platform is used to store and visualize the captured data from a standalone photovoltaic system. The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. The results are been compared with results obtained by another machine learning algorithm such as polynomial regression and case-based reasoning. Further, a website is developed wherein the user can key in the date and time. The output of that transaction is predicted temperature, humidity, and forecasted power generation of the specific standalone photovoltaic system. The presented results and obtained characteristics confirm the superiority of the proposed techniques in predicting power generation.

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An energy management system (EMS) is a system of computer-aided tools used by operators of electric utility grids to monitor, control, and optimize the performance of the generation and/or transmission system. In this paper, an IOT based power management system is proposed for standalone photovoltaic (SAPV) system, which involves loads that are categorized based on priorities as emergency, critical, essential and convenient. The Internet of Things (IOT) based EMS is realized to provide proper and convenient load shedding, source management, data acquisition and control of the SAPV networks. The load prediction in SAPV networks is handled using LabVIEW. The EMS is designed to diagnose the normal and overcurrent conditions in the network. During overcurrent faults, the loads are automatically disconnected and the load status at any instant is sent to the registered email. The user is able to access the remote SAPV networks, control the loads and restore the network operation using mobile app. The proposed system is validated and tested on 2-bus and 3-bus SAPV networks.