Abstract
In recent years, researchers have directed more attention towards accurately predicting and maintaining stable loads, recgnizing their profound impact on the economy and the crucial need for effective power system management. However, the majority of past studies have focused solely on either decreasing forecast errors or improving stability, with few delving into both simultaneously. Developing a forecasting model that addresses both objectives concurrently presents a formidable task, primarily due to the intricate nature of load behavior patterns. Hence, in order to concurrently accomplish both objectives, we propose and implement an Artificial intelligence based multi objective algorithm (AIMOA). The suggested model demonstrates superior performance compared to baseline models across different real-world electricity datasets, with results indicating strong performance of our proposed approach.
