Artificial Intelligence-Based Load Forecasting and Energy Scheduling in Smart Microgrids

Abstract

This paper examines the use of Artificial Intelligence (AI) methods, namely Long Short-Term Memory (LSTM) networks and Particle Swarm Optimization (PSO) in enhancing load forecasting and energy scheduling of smart microgrids. The study reveals that LSTM models are effective in improving the accuracy of load forecasting in suggesting complex temporal dependencies in energy consumption datasets, which are not achieved in the traditional approaches like ARIMA and Linear Regression. Also, the paper discusses the application of PSO to energy scheduling, where it is demonstrated that the methodology can improve operational expenditures, maximize the employment of renewable energy and decrease the need of taking power off the grid. The findings demonstrate that the AI-based solutions have great potential in the context of optimizing energy management in microgrids, including the use of renewable energy and energy efficiency improvement and the possibility to introduce energy sustainability. The results enable the significance of applying AI-based methods to tackle power intermittency, cost, and grid-free issues to define the future of energy systems.

Keywords:  Artificial Intelligence, Load Forecasting, Energy Scheduling, Smart Microgrids, LSTM, Particle Swarm Optimization, Renewable Energy, Cost Reduction, Grid Reliance, Energy Management