Energy Demand Forecasting Techniques Comparison Using Python

I am in search of a proficient freelancer who can guide me through the process of learning and constructing coding frameworks for advanced energy demand forecasting methodologies to analyze the linear and non-linear trends of weather, urbanization, GDP & price with energy demand. My focus is on leveraging statistical and machine learning techniques for a trio of applications: energy demand(consumption ) forecasting and production/imports gap. Key Elements of the Project: - Introduction to and code development for ARIMAX, SARIMA, MARS, Random Forest, and XGBOOST models. - Application of these models to energy production/imports demand analysis, with a specific eye on predicting energy consumption and forecasting demand. - Utilization of diverse data sets including historical energy consumption data, relevant weather data, and pertinent economic indicators to train and test models. Ideal Skills and Experience: - Proficiency in Python programming, especially in the context of machine learning, AI, and deep learning applications. - Solid understanding of energy forecasting models mentioned (ARIMAX, SARIMA, MARS, Random Forest, and XGBOOST) and their practical implementations. - Experience in handling and analyzing complex data sets, specifically those related to energy, weather, and economic indicators. - Ability to guide a learner through the technical aspects of coding for energy forecasting, ensuring the concepts are clearly understood and executable. My aspiration through this project is not only to master the technical coding skills required for these energy forecasting techniques but also to understand their practical applications and implications in the context of energy demand(consumption ) forecasting and production/imports gap.