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.

本文针对具有可加性可分离效用函数的基于随机效用最大化的多重离散连续（MDC）选择模型（例如多重离散连续极值（MDCEV）模型）提出了一种简单且计算效率高的预测算法。该算法基于Karush Kuhn-Tucker（KKT）最优性条件的简单而有见地的分析探索，为模型的性质提供了新的亮点。将本文提出的MDCEV模型和预测算法应用于家庭级能耗数据集，以分析美国的住宅能耗模式。更多，