With the widespread adoption of smart meters in buildings, an unprecedented amount of high-resolution energy data is released, which provides opportunities to understand building consumption patterns. Accordingly, research efforts have employed data analytics and machine learning methods to segment customers based on their load profiles, which help utilities and energy providers promote customized/personalized targeting for energy programs. Existing energy consumption segmentation techniques use assumptions that could reduce clusters’ quality in representing their members. Therefore, in this paper, we investigated a two-stage clustering method for capturing more representative load shape temporal patterns and peak demands through a cluster merging approach. In the first stage, load shapes are clustered (using classical clustering algorithms) by allowing a large number of clusters to accurately capture variations in energy use patterns, and cluster centroids are extracted by accounting for limited shape misalignment within the range of Demand Response (DR) timeframes. In the second stage, clusters with similar centroids and power magnitude ranges are merged using Complexity-Invariant Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the efficacy of the framework, compared to baseline methods, and discuss the impact on energy management. The proposed investigated merging-based clustering also increased correlation between cluster centroids and the corresponding members by 3–9% for different datasets.

With the increased adoption of distributed energy resources (DERs) and renewables, such as solar panels at the building level, consumers turn into prosumers with generation capability to supply their on-site demand. The temporal complementarity between supply and demand at the building level provides opportunities for energy exchange between prosumers and consumers towards community-level self-sufficiency. Investigating different aspects of community-level energy exchange in cyber and physical layers has received attention in recent years with the increase in renewables adoption. In this study, we have presented an in-depth investigation into the impact of energy exchange through the quantification of temporal energy deficit–surplus complementarity and its associated self-sufficiency capacities by considering the impact of variations in community infrastructure configurations, variations in household energy use patterns, and the potential for user adaptation for load flexibility. To this end, we have adopted a data-driven simulation using real-world data from a case-study neighborhood consisting of ~250 residential buildings in Austin, TX with a mix of prosumers and consumers and detailed data on decentralized DERs. By accounting for the uncertainties in energy consumption patterns across households, different levels of PV and energy storage integration, and different modalities of user adaptation, various scenarios of operations were simulated. The analysis showed that with PV integration of more than 75%, energy exchange could result in self-sufficiency for the entire community during peak generation hours from 11 a.m. to 3 p.m. However, there are limited opportunities for energy exchange during later times with PV-standalone systems. As a potential solution, leveraging building-level storage or user adaptation for load shedding/shifting during the 2-h low-generation timeframe (i.e., 5–7 p.m.) was shown to increase community self-sufficiency during generation hours by 17% and 5–10%, respectively, to 83% and 71–76%.

Power utilities leverage Demand Response (DR) events to effectively reduce the peak load at critical times with excessive power demand. DR programs are generally categorized as manual or automated from the automation perspective. The opportunities for automated DR in the residential sector have emerged with the integration of smart and connected loads. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers’ direct involvement, given that many consumers might not engage sufficiently to participate in the manual DR. However, it has been shown that unjustified load shifting from many consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target consumers for participation according to efficacy criteria. To address this issue, in this paper, we propose a data-driven method for the selection of consumers according to their potential for demand reduction in a DR program. The proposed method characterizes the frequency, consistency, and the peak time usage of deferrable loads across several subsequent days. By measuring the impact on peak-load shaving, we evaluated our approach on a subset of electricity dataset from the Pecan Street Dataport. The findings demonstrate the efficacy of the proposed method in selecting consumers with deferrable loads based on their potential for demand reduction in future events.

Providing users’ location information in indoor environments has great importance for the management of the built environment in energy conservation efforts, facilitating navigation in large and complex places, providing context-aware information, and addressing safety concerns in emergency situations. Various technologies have been proposed to address the problem of indoor positioning among which is the emerging positioning techniques based on LED lights. LED lighting has the capability for visible light communication (VLC), which in turn could enable visible light positioning (VLP). Recent research has shown that VLP can achieve highly accurate results compared to systems based on RF waves. In this paper, the scalability of using an LED- based indoor positioning system for a typical office environment is studied. A trilateration positioning method is applied to localize the user. The effect of using different number of lights and specific hardware parameters that can affect the communication range of the system has been investigated. The findings were presented in terms of accuracy at different points and attainable area for localization. The findings show that the scalability and performance of the positioning system rely on the selection of these parameters, and highly accurate positioning can be achieved in an office setting through adjusting the configuration of lights and parameter selection.