Perceived-Value-driven Optimization of Energy Consumption in Smart Homes
Perceived-Value-driven Optimization of Energy Consumption in Smart Homes
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DOI：
10.1145/3375801
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发表时间：
2020-04
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影响因子：
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通讯作者：
A. R. Khamesi;S. Silvestri;D. A. Baker;Alessandra De Paola
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文献类型：
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作者：
A. R. Khamesi;S. Silvestri;D. A. Baker;Alessandra De Paola
在过去的几十年里，住宅能源消耗一直在迅速增长。已经进行了一些研究工作来减少住宅能源消耗，包括需求响应和智能住宅环境。然而，最近的研究表明，这些方法实际上可能会导致整体消耗的增加，这是由于人类用户与这些能源管理系统交互时发生的复杂心理过程。在这篇文章中，使用跨学科的方法，我们介绍了一个感知价值驱动的框架，在智能住宅环境中的能源管理，考虑用户如何感知不同的设备的价值，以及如何使用一些设备是偶然的使用其他。我们定义了一个感知价值的用户效用作为一个线性规划（ILP）的问题。我们证明了这个问题是NP-难的，并提供了一个启发式的方法，称为CONDensed DependencY（CODY）。我们使用合成和真实的数据集，大规模的在线实验，并在密苏里州科技大学太阳能村的真实现场实验验证我们的结果。仿真结果表明，我们的方法达到了接近最优的性能，并显着优于以前提出的解决方案。我们的在线和真实现场实验的结果也表明，与以前的方法相比，用户在很大程度上更喜欢我们的解决方案。
Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the use of some appliances are contingent on the use of others. We define a perceived-value user utility used as an Integer Linear Programming (ILP) problem. We show that the problem is NP-Hard and provide a heuristic method called COndensed DependencY (CODY). We validate our results using synthetic and real datasets, large-scale online experiments, and a real-field experiment at the Missouri University of Science and Technology Solar Village. Simulation results show that our approach achieves near optimal performance and significantly outperforms previously proposed solutions. Results from our online and real-field experiments also show that users largely prefer our solution compared to a previous approach.