Power demand estimation techniques applied to microgrid

Abstract

ABSTRACT The increasing global demand for power necessitates a focus on renewable energy sources to reduce environmental impact and diversify energy generation. Estimating power demand in real-time electrical systems is crucial in this context. However, the literature contains various estimation techniques without clear guidance on the most suitable option based on performance and field-specific use. To address this gap, a study compares these techniques and identifies the most effective method for power demand estimation through a training process that assesses the accuracy of predictions against actual values. This comparison is conducted using the JUPYTER platform and historical data from the ENTSO-E power systems database. Results indicate that Linear Regression and Support Vector Machine techniques are commonly used for model evaluation, but the Adaptive Neural Network consistently performs as the most robust estimation method. The choice of technique depends on researcher preference, but it's essential to note that inadequate data preparation can lead to suboptimal results. In summary, as the world's power demand rises, the study identifies the Adaptive Neural Network as a promising tool for accurate power demand estimation, emphasizing the significance of proper data preparation in this process.