Computationally Efficient Forecasting Procedures for Kuhn-Tucker Consumer Demand Model Systems: Application to Residential Energy Consumption Analysis

This paper proposes simple and computationally efficient forecasting algorithms for a KuhnTucker (KT) consumer demand model system called the Multiple Discrete-Continuous Extreme Value (MDCEV) model. The algorithms build on simple, yet insightful, analytical explorations with the Kuhn-Tucker conditions of optimality that shed new light on the properties of the model. Although developed for the MDCEV model, the proposed algorithm can be easily modified to be used for other KT demand model systems in the literature with additively separable utility functions. 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.