Forecasting And Optimisation For Microgrid In Home Energy Management Systems

The wide proliferation of renewable energy and deregulation of power grid systems require small power utilization systems to deploy intelligent methods of adjustment to the user power demand. To accomplish this goal, the smart power demand forecasting and power consumption optimization methods and algorithms need to be developed. For this purpose, small power utilization systems can benefit from the techniques developed for the smart grid in general. The present paper is devoted to the development of a forecasting model based on the Long Short-Term Memory (LSTM) method and an optimization model based on Genetic Algorithm (GA) adopted for the use in home energy management systems (HEMS). The present work describes a smart microgrid architecture with a focus on LSTM and GA. The experiments demonstrate that the developed algorithms generate a stable pattern of daily power demand. The use of the developed algorithms allows automated shifting of power to achieve the lowest price without sacrificing their comfort. The main contributions of the present work are the inclusion of all parts of the smart microgrid architecture (non-invasive load identification, forecasting, optimization, renewable energy sources and storage elements) in the research proposing a fully automated control in HEMS rather than recommendation based only.