Computationally efficient forecasting procedures for Kuhn-Tucker consumer demand model systems: Application to residential energy consumption analysis

Abstract

This paper proposes simple and computationally efficient forecasting algorithms for random utility maximization-based multiple discrete-continuous (MDC) choice models with additively separable utility functions, such as the Multiple Discrete-Continuous Extreme Value (MDCEV) model. The algorithms build on simple yet insightful, analytical explorations with the Karush Kuhn-Tucker (KKT) conditions of optimality that shed new light on the properties of the models. The MDCEV model and the forecasting algorithms proposed in this paper are applied to a household-level energy consumption dataset to analyze residential energy consumption patterns in the United States. Further, simulation experiments are undertaken to assess the computational performance of the proposed and existing KT demand forecasting algorithms for a range of choice situations with small and large choice sets.