Energy Consumption Forecasting in Smart Cities Using Predictive Analysis

Energy management in smart cities is a critical challenge due to the increasing population, urbanization, and growing energy demand. Efficient energy forecasting mechanisms are vital to optimize consumption, enhance sustainability, and ensure a balanced energy supply. This paper presents an energy consumption forecasting approach tailored for smart cities, leveraging advanced predictive analysis techniques. By employing machine learning models, the system forecasts energy consumption patterns based on historical data, real-time data streams, and environmental factors. The aim is to help ur-ban authorities and policymakers manage energy resources more effectively while improving energy efficiency in smart city infrastructures. This paper investigates the accuracy and performance of two predictive models for en-ergy forecasting: a Support Vector Regression (SVR) model and an Artificial Neural Network (ANN). The study compares the performance of these models in terms of forecast accuracy, computational efficiency, and adaptability to real-time data. Extensive testing is performed on simulated datasets to assess the models under different environmental conditions. Finally, the paper dis-cusses the implications of these models for energy management and decision-making in smart cities.