Intelligent Load Control with Embedded Automation and Mobile Application

The escalating rate of energy consumption and its adverse environmental effects, coupled with the reliance on non-renewable sources like fossil fuels, has heightened the importance of energy management. The framework for "Intelligent Load Control with Embedded Automation and Mobile Application" is presented, emphasizing the optimization of energy consumption. Utilizing Long short-term memory (LSTM) model from deep learning, to implement energy generation forecasting. These precise forecasts interface with an advanced load scheduling algorithm implemented by using Autoregressive Integrated Moving Average (ARIMA) Model, prioritizing loads based on priority matrix technique and efficiently scheduling loads based on predefined criteria and temporal proximity. Further, a controller enables dynamic load shifting between renewable sources and the grid further this will control the loads as per scheduled timetable, enhancing overall energy utilization. The integration of these methodologies, along with a mobile application for real-time monitoring, establishes an intelligent system poised to significantly improve energy efficiency and promote sustainable practices in the context of embedded automation and mobile applications.