Machine Learning based Load and Temperature Behavior Clustering and Peak Shifting Implementation on Bangladeshi Grid Data

Abstract

With a continual rise in electricity prices, the integration of demand-side management (DSM) techniques such as peak load shifting and load behavior patterns with machine learning-based solutions has become a necessity in modern grid management. In this paper, using the Pearson correlation coefficient (PCC), the correlation between a synthesized dataset consisting of load consumption data from the national grid of Bangladesh, and meteorological data, such as maximum and minimum temperature has been calculated, which resulted in values of 0.84, 0.87 and 0.89, respectively. Next, the yearly load data have been clustered using a k-Means clustering algorithm, to find consumption patterns, and using the labels, temperature ranges are clustered to suggest temperature dependence, in accordance with consumption patterns. Finally, for every cluster, using a hypothetical set of percentages, a peak shaving, and load shifting algorithm has been implemented to show hypothetical approximates of load shifting potential for every year, which produced percentages 8.83, 9.07, and 8.79 for the years 2018, 2019 and 2021, respectively.