Energy Monitoring with Trend Analysis and Power Signature Interpretation

Abstract

Energy consumption and the science behind the patterns in electricity expenditure have been rapidly growing fields in technology for decades owing to the vital nature of fuel. This research explores room for advancement in exploiting consumption data and generating smart results powered by state-of-the-art algorithms and sensors. The line of experimentation for this problem statement is focused on enabling users to interpret their power consumption efficiently and interacts with live visual trends and limits their consumption to a customized estimate. Prediction models for forecasting consumption, analysing power signature of high-load appliances, and monitoring of appliance power state have been built based on parameters such as instantaneous and cumulative energy, power, and solar energy. Although the scope of this setup is limited to individual accommodations, the purpose of this research is to extrapolate the functionalities to an industrial scale, where the importance of restricted consumption, and protection against casualties is truly evident. The proposed model improves the accuracy of time series forecasting of energy consumption as compared to existing forecasting models.