A Review of Load Demand Estimation and Prediction Models (LDEPMs) For Microgrid Projects

Abstract

The global shift towards sustainable and reliable energy sources projects has prompted an increasing focus on load demand estimation and prediction. These projects can potentially transform the lives of millions by providing access to electricity. The purpose of this review article is to keep readers up to date on the most recent developments in load demand estimation and prediction for microgrid projects, in order to ensure optimal power management. This review article will serve as a basis for evidence-based decision-making and provide significant insights for policymakers, researchers, and professionals engaged in the building and sustainability of micro-grid initiatives in rural communities. The study highlights how crucial it is to make the choice of selecting relevant input variable (factors) of a load demand estimation and prediction model (LDEPM) as rational selection of input variables (factors) based on experience could be imperative. The study also centered on employing robust data pre-processing techniques, utilizing advanced methodologies. The study highlights the benefits of optimizing load demand estimation and prediction models for energy management, system efficiency, and reliability using artificial intelligence methods and its integration with machine learning and other metaheuristic techniques for improved prediction accuracy. The study also emphasizes on the need for risk management for load demand estimation and prediction models to ensure reliable and sustainable energy supply. Findings from the studies will serve as a basis for evidence-based decision-making and offers important insights to practitioners, researchers, and policymakers involved in the development and sustainability of microgrid initiatives in rural locations.