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The large scale deployment of Advanced Metering Infrastructure among residential energy customers has served as a boon for energy systems research relying on granular consumption data. Residential Demand Response aims to utilize the…

Systems and Control · Computer Science 2016-07-05 Datong Zhou , Maximilian Balandat , Claire Tomlin

In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and…

Signal Processing · Electrical Eng. & Systems 2020-08-10 Abdelkareem Jaradat , Hanan Lutfiyya , Anwar Haque

The expansion of residential demand response programs and increased deployment of controllable loads will require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe…

Applications · Statistics 2018-10-10 Yuting Ji , Elizabeth Buechler , Ram Rajagopal

Residential customers have traditionally not been treated as individual entities due to the high volatility in residential consumption patterns as well as a historic focus on aggregated loads from the utility and system feeder perspective.…

Residential Demand Response has emerged as a viable tool to alleviate supply and demand imbalances of electricity, particularly during times when the electric grid is strained due a shortage of supply. Demand Response providers bid…

Computer Science and Game Theory · Computer Science 2017-09-05 Datong P. Zhou , Maximilian Balandat , Munther A. Dahleh , Claire J. Tomlin

Enhancing the spatio-temporal observability of residential loads is crucial for achieving secure and efficient operations in distribution systems with increasing penetration of distributed energy resources (DERs). This paper presents a…

Systems and Control · Electrical Eng. & Systems 2019-10-08 Shanny Lin , Hao Zhu

In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her…

Machine Learning · Computer Science 2017-02-01 Mikhail V. Goubko , Sergey O. Kuznetsov , Alexey A. Neznanov , Dmitry I. Ignatov

Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate change, securing water resources and supply has emerged as an…

Smart meter data analysis can provide insights into residential electricity consumption behaviors. Seasonal variation in consumption is not well understood but yet important to utilities for energy pricing and services. This paper aims to…

Applications · Statistics 2021-12-03 Zhenyu Wang , Hao Wang

Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high…

Machine Learning · Statistics 2020-03-09 Andrés M. Alonso , F. Javier Nogales , Carlos Ruiz

Electricity load consumption may be extremely complex in terms of profile patterns, as it depends on a wide range of human factors, and it is often correlated with several exogenous factors, such as the availability of renewable energy and…

Machine Learning · Computer Science 2025-02-03 Aleksei Kychkin , Georgios C. Chasparis

The recent advent of smart meters has led to large micro-level datasets. For the first time, the electricity consumption at individual sites is available on a near real-time basis. Efficient management of energy resources, electric…

Applications · Statistics 2014-09-10 Siddharth Arora , James W. Taylor

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the…

Machine Learning · Computer Science 2019-05-31 Nameer Al Khafaf , Mahdi Jalili , Peter Sokolowski

The wide adoption of smart meters makes residential load data available and thus improves the understanding of the energy consumption behavior. Many existing studies have focused on smart-meter data analysis, but the drivers of energy…

Machine Learning · Computer Science 2021-06-11 Zhuo Wei , Hao Wang

Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such as Synthesized Load Profiles, which treat residential…

Computers and Society · Computer Science 2025-03-10 Daniel R. Bayer , Felix Haag , Marco Pruckner , Konstantin Hopf

The smart meter data analysis contributes to better planning and operations for the power system. This study aims to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on the consumption…

Machine Learning · Computer Science 2021-11-03 Wenjun Tang , Hao Wang , Xian-Long Lee , Hong-Tzer Yang

The rapid expansion of Advanced Meter Infrastructure (AMI) has dramatically altered the energy information landscape. However, our ability to use this information to generate actionable insights about residential electricity demand remains…

Computers and Society · Computer Science 2022-04-25 Xiao Chen , Chad Zanocco , June Flora , Ram Rajagopal

The dynamics of power consumption constitutes an essential building block for planning and operating energy systems based on renewable energy supply. Whereas variations in the dynamics of renewable energy generation are reasonably well…

One of the major barriers for the retailers is to understand the consumption elasticity they can expect from their contracted demand response (DR) clients. The current trend of DR products provided by retailers are not consumer-specific,…

Systems and Control · Electrical Eng. & Systems 2021-11-26 Kamalanathan Ganesan , João Tomé Saraiva , Ricardo J. Bessa

Residential buildings account for a significant portion (35\%) of the total electricity consumption in the U.S. as of 2022. As more distributed energy resources are installed in buildings, their potential to provide flexibility to the grid…

Machine Learning · Computer Science 2024-08-13 Patrick Salter , Qiuhua Huang , Paulo Cesar Tabares-Velasco
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