Publication

Abstract : This paper integrates Fourier regression with Neural networks to enhance the analysis and prediction of energy consumption patterns. The Fourier-Neural model leverages the strength of Fourier analysis in identifying periodic trends and harmonics in time-series data, combined with the adaptive learning capabilities of deep neural networks to model complex, nonlinear relationships. The methodology begins with decomposing energy consumption data into its frequency components using Fourier transforms, which are then used as inputs to a neural network model to predict future energy usage. The paper explores the implications of integrating harmonics and neural computation in energy pattern analysis, emphasizing the potential for enhancing predictive analytics in the energy sector. The findings indicate that Fourier regression can significantly improve the accuracy of energy consumption forecasts, compared to traditional linear and polynomial regression models. In addition, the study presents a comprehensive evaluation of a neural network model developed to predict per-capita consumption over time. Initial results exhibit high accuracy, with the model achieving near-perfect performance across training, validation, and test datasets, as evidenced by error histograms and regression plots that demonstrate small error margins. The Fourier-Neural approach presents a scalable and efficient solution for energy consumption forecasting, providing a robust tool for energy analysts, policymakers, and utility companies to make informed decisions. By bridging the gap between traditional Fourier analysis and modern machine learning, this research paves the way for advanced analytical frameworks in energy pattern analysis and beyond.