Residential consumers can use the demand response program (DRP) if they can
utilize the home energy management system (HEMS), which reduces consumer costs
by automatically adjusting air conditioning (AC) setpoints and shifting some
appliances to off-peak hours. If HEMS knows occupancy status, consumers can
gain more economic benefits and thermal comfort. However, for the building
occupancy status, direct sensing is costly, inaccurate, and intrusive for
residents. So, forecasting algorithms could serve as an effective alternative.
The goal of this study is to present a non-intrusive, accurate, and
cost-effective approach, to develop a multi-objective simulation model for the
application of DRPs in a smart residential house, where (a) electrical load
demand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints,
and (c) , worst cases scenario approach is very conservative. Because that is
unlikely all uncertain parameters take their worst values at all times. So, the
flexible robust counterpart optimization along with uncertainty budgets is
developed to consider uncertainty realistically. Simulated results indicate
that considering uncertainty increases the costs by 36 percent and decreases
the AC temperature setpoints. Besides, using DRPs reduces demand by shifting
some appliance operations to off-peak hours and lowers costs by 13.2 percent.

该研究提出了一种多目标仿真模型和不侵入式的基于占用状态预测的方法，以开发一种适用于智能住宅的 DRP 应用程序，其中同时考虑了不确定性和鲁棒性，并通过将一些电器操作移至低峰时段，降低了成本 13.2％，并降低空调温度设定点。

考虑居民舒适性和隐私的需求响应计划成本优化

Optimization of Residential Demand Response Program Cost with  Consideration for Occupants Thermal Comfort and Privacy

The present study explores the use of clustering techniques for the design
and implementation of a demand response (DR) program for commercial and
residential prosumers. The goal of the program is to shift the participants'
consumption behavior to mitigate two issues a) the reverse power flow at the
primary substation, that occurs when generation from solar panels in the local
grid exceeds consumption and b) the system wide peak demand, that typically
occurs during hours of the late afternoon. For the clustering stage, three
popular algorithms for electrical load clustering are employed -- namely
k-means, k-medoids and a hierarchical clustering algorithm -- alongside two
different distance metrics -- namely euclidean and constrained Dynamic Time
Warping (DTW). We evaluate the methods using different validation metrics
including a novel metric -- namely peak performance score (PPS) -- that we
propose in the context of this study. The best setup is employed to divide
daily prosumer load profiles into clusters and each cluster is analyzed in
terms of load shape, mean entropy and distribution of load profiles from each
load type. These characteristics are then used to distinguish the clusters that
would be most likely to aid with the DR schemes would fit each cluster.
Finally, we conceptualize a DR system that combines forecasting, clustering and
a price-based demand projection engine to produce daily individualized DR
recommendations and pricing policies for prosumers participating in the
program. The results of this study can be useful for network operators and
utilities that aim to develop targeted DR programs for groups of prosumers
within flexible energy communities.

采用聚类技术实现需求响应节能项目，并结合预测、聚类和价格策略引擎为参与项目的生产者提供个性化的节约建议和定价策略。