A Forecasting Approach for IoT-Based Energy and Power Quality Monitoring in Buildings

This paper proposes a novel energy consumption and power quality forecasting technique based on the combination of poly-exponential (PE) and random forest (RF). This technique paves away seamlessly integrating the forecast to the Internet-of-things (IoT)-based monitoring system due to its capability of improving computational efficiency. For the experimental setup, the monitoring system in a building supplied the data for forecasting consisting of daily energy consumption and power quality parameters. The developed algorithm for efficient computational forecasting has already considered the space limitation in online real-time monitoring with a minor effect on the model prediction error (less than 0.3% in our experimental setup). The algorithm can alert the user once an indication of energy waste (more than the determined boundary) or the actual voltage violated the tolerance might occur. An experimental dataset taken from the Bappeda Pontianak building in West Kalimantan, Indonesia, shows that the proposed algorithm required fewer samples than the RF yet could predict the daily energy consumption and voltage inside a building accurately up to 96%.