IoT Based Energy Management Solution for Smart Green Buildings

Energy Management Systems (EMS) provide information on energy usage, especially which device is consuming how much energy for monitoring and control. These EMS can be substantially improved and enhanced through the use of Internet of Things (IoT) based energy monitoring technology to save more energy. This research proposes a real-time IoT based energy management system for smart green buildings. The proposed system contains three main phases, including measuring power consumption, forecasting power consumption, and face recognition. The method of forecasting used in this research is Short-Term Load Forecasting (STLF), based on the K-Nearest Neighbor (KNN) algorithm. There are six variables from Digital Power Meter (DPM) required as reference data to train the prediction methods, including Line Current A, Line Current B, Line Current C, Line Voltage A, Line Voltage B, and Line Voltage C. The forecasted result determines the power consumption of the smart building for the next hours of the same day. The active, reactive, and apparent powers are calculated based on the forecasted result. Face recognition in a smart building can prevent unauthorized persons from entering a certain area of a smart building. The method used in face recognition is based on the Viola-Johns algorithm. The results obtained from the accuracy of the Viola-Johns classifier based on Haar features indicate that the system can perfectly detect and recognize faces with a total accuracy of 90%. The True Negative Rate (TNR), Positive Predictive Value (PPV) and False Discovery Rate (FDR) were found to be 50%, 69.4%, and 30.5%, respectively.