In this modern world, electricity plays a vital role. It is essential for human life and also affects normal behavior of environment resulting in global warming. Recent developments in artificial intelligence (AI), in particular machine learning (ML), have been significantly advancing smart city applications. Smart infrastructure, which is an essential component of smart cities, is equipped with power systems designed for optimizing smart devices. In this article, real domestic consumption data of 500 consumers from TANGEDCO are analyzed and clustered based on different seasons (consumption rate varies upon different weather conditions) for smart city applications. An efficient clustering algorithm k-means integrates big data set for a period of 10 years and converts it into clustering graph with three seasons. By analyzing this data, the amount of consumption of electricity by humans in particular area (Pasupathikovil) of Papanasam taluk of Thanjavur district will be predicted. This article would be more useful for predicting changes in usage of electricity and take proper steps for analyzing the consumption accordingly and it will be more useful in smart city development. It gives an idea of which season needs more consumption and which needs less.

中文翻译：

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基于季风数据的智能城市用电模型机器学习方法

在这个现代世界中，电力起着至关重要的作用。它对人类生活至关重要，也会影响环境的正常行为，导致全球变暖。人工智能 (AI) 的最新发展，尤其是机器学习 (ML)，极大地推动了智慧城市的应用。智能基础设施是智慧城市的重要组成部分，配备了专为优化智能设备而设计的电力系统。在本文中，针对智慧城市应用，TANGEDCO 500 名消费者的实际国内消费数据基于不同季节（消费率因天气条件而异）进行分析和聚类。一种高效的聚类算法k-means将10年的大数据集整合成三个季节的聚类图。通过分析这些数据，将预测坦贾武尔地区 Papanasam taluk 特定地区 (Pasupathikovil) 的人类用电量。这篇文章将更有助于预测用电量的变化，并相应地采取适当的步骤分析消耗量，对智慧城市的发展更有用。它给出了哪个季节需要更多消费以及哪个需要更少消费的想法。