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Abstract:
Rising demand, decreased efficiency, shifting supply and demand patterns, and a dearth of analytics hamper efforts to achieve optical management in the global energy sector. Countries with growing economies face greater difficulty with these issues. Because of the widespread usage of unofficial connections to the power grid, a significant proportion of electricity is either not metered or billed, leading to waste and increased CO2 emissions because users have little incentive to conserve power they don’t have to pay for. Artificial intelligence and associated technologies that enable communication between smart grids, smart metres, and Internet of Things devices have already begun to be used by the power sector in industrialised nations. Technology advancements in these areas have the potential to boost the usage of renewable energy sources, as well as improve power. Thus the aim of this work is to use machine learning in electrical grids In the context of electrical grids, The method used is the k-means clustering which is applied to identify patterns and anomalies in power consumption data. Specifically, k-means clustering can be used to group households or buildings based on their power consumption profiles, which can provide insights into energy usage patterns and help utilities better allocate resources. K-means clustering can also be applied to identify abnormal power consumption patterns, which may indicate issues such as equipment malfunction or fraud. The aim of this thesis is to indicate that Artificial Intelligence could cause a major shift in the power industry by helping utilities streamline their operations, cut costs, and boost efficiency and reliability. Machine Learning’s application in the sector may one day lead to cheaper rates of utilities and greener, more reliable power for consumers.
