Charles E. Via Jr. Department of Civil and Environmental Engineering

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https://hdl.handle.net/10919/23747
The Charles E. Via, Jr. Department of Civil and Environmental Engineering, which is ranked in the top 10 accredited civil and environmental engineering departments by the US News and World Report survey, is one of the largest programs in the United States. The Department has 46 full-time faculty, 657 undergraduate, and 400 graduate students. Civil engineers are the principal designers, constructors, operators, and caretakers of many of the constructed facilities and systems that contribute to the high quality of life enjoyed in the United States. The Charles E. Via, Jr. Department of Civil and Environmental Engineering offers educational programs in all areas of civil engineering practice.

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Browsing Charles E. Via Jr. Department of Civil and Environmental Engineering by Author "Afzalan, Milad"

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    A Machine Learning Framework to Infer Time-of-Use of Flexible Loads: Resident Behavior Learning for Demand Response
    Afzalan, Milad; Jazizadeh, Farrokh (IEEE, 2020-06-26)
    Load shapes obtained from smart meter data are commonly utilized to understand daily energy use patterns for adaptive operations in applications such as Demand Response (DR). However, they do not provide information on the underlying causes of specic energy use patterns i.e., inference on appliances' time-of-use (ToU) as actionable information. In this paper, we investigated a scalable machine learning framework to infer the appliances' ToU from energy load shapes in a collection of residential buildings. A scalable and generalized inference model obviates the need for model training in each building to facilitate its adoption by relying on training data from a set of previously observed buildings with available appliance- level data. To this end, we demonstrated the feasibility of using load shape segmentation to boost ToU inference in buildings by learning from their nearest matches that share similar energy use patterns. To infer an appliance ToU for a building, classication models are trained for inference on subintervals of load shapes from matched buildings with known ToU. The framework was evaluated using real-world energy data from Pecan Street Dataport. The results for a case study on electric vehicles (EV) and dryers showed promising performance by using 15-min smart meter load shape data with 83% and 71% F-score values, respectively, and without in-situ training.
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    Quantification of Demand-Supply Balancing Capacity among Prosumers and Consumers: Community Self-Sufficiency Assessment for Energy Trading
    Afzalan, Milad; Jazizadeh, Farrokh (MDPI, 2021-07-17)
    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%.
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    Two-Stage Clustering of Household Electricity Load Shapes for Improved Temporal Pattern Representation
    Afzalan, Milad; Jazizadeh, Farrokh; Eldardiry, Hoda (IEEE, 2021-11-16)
    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.