Energy Modeling: A Comparison of Statistical Methods and Artificial Neural Networks for Electricity Load Forecasting

This article delves into the prediction of electricity consumption in urban environments, with a specific focus on Phoenix, Arizona, during the upcoming summer. Traditional modeling methods, though widely utilized, often lack the nuanced capabilities required to capture the complex relationships between meteorological variables and energy demand. In contrast, artificial intelligence, specifically artificial neural networks, emerges as a potent solution for overcoming the limitations of traditional approaches. The dataset incorporates historical electricity usage data and meteorological factors like temperature, humidity, and wind speed. Through a comparative analysis, this study demonstrates the superior predictive performance of artificial neural networks over traditional methods. The neural network model effectively learns intricate patterns within the data, resulting in accurate forecasts of electricity consumption. The findings underscore the indispensable role of machine learning, particularly neural networks, in optimizing resource allocation, achieving cost reduction, and enhancing grid stability. The integration of meteorological data with advanced modeling techniques not only improves predictive accuracy but also empowers city officials and grid operators with valuable insights for informed decision-making. This innovative approach signifies a paradigm shift in energy consumption prediction, emphasizing the necessity of machine learning methods, especially neural networks, for achieving unparalleled precision in urban environments like Phoenix.