SmartPeak: Peak Shaving and Ambient Analysis For Energy Efficiency in Electrical Smart Grid

Abstract

In modern times, buildings are heavily contributing to the overall energy consumption of the countries and in some countries they account up to 45% of their total energy consumption. Hence a detailed understanding of the dynamics of energy consumption of buildings and mining the typical daily electricity consumption profiles of households in buildings can open up new avenues for smart energy consumption profiling. This can open up newer business opportunities for all stakeholders in energy supply chain thereby supporting the energy management strategies in a smart grid environment and provide opportunities for improvement in building infrastructure with fault detection and diagnostics. In this context, we propose a approach to predict and re-engineer the hourly energy demand in a residential building. A data-driven system is proposed using machine learning techniques like Multi Linear Regression and Support Vector Machine to predict electricity demand in a smart building along with a real-time strategy to enable the users to save energy by recommending optimal scheduling of the appliances at times of peak load demand, given the consumer's constraints.