Understanding patterns of thermostat overrides after demand response events

Tomat, Valentina; Vellei, Marika; Ramallo-González, Alfonso P.; González-Vidal, Aurora; Dréau, Jérôme Le; Skarmeta-Gómez, Antonio F.

doi:10.1016/j.enbuild.2022.112312

Cited by 12 publications

(5 citation statements)

References 42 publications

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“…The method is tested on a cluster of Belgian houses to provide a secondary reserve to solve a winter imbalance. Results show that a load aggregator using 100 heat pumps could expect to harvest up to 138 [204] This paper suggests a DR control algorithm for HVAC units that uses Lyapunov optimization to decrease nonrenewable power demand and maintain customer comfort. The algorithm uses a distributed strategy that is less expensive and more secure.…”

Section: Republic Of Koreamentioning

confidence: 99%

A Survey on Direct Load Control Technologies in the Smart Grid

Ozkop

2024

IEEE Access

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The purpose of this paper is to review the scientific literature on direct load control (DLC) technology in the context of a smart grid (SG). This study synthesized and compared various articles from the literature with the aim of conducting a comprehensive analysis. This study aims to provide a detailed understanding of the landscape, recent advancements, and the relationships among various aspects within this field. The evaluation is conducted through a systematic literature review, examining recent advancements in the field. A survey of over 200 papers published between 1983 and 2022 is classified based on appliance information, aim/objective, constraint, management method and tool, implementation, and publication status. In the DLC application, the study identifies the type and population of appliances used in and the aim/objective and constraint considered. It also determines whether forecast, prediction, and estimation structures, optimization and control algorithms, and specific structures are employed. The practical aspect of the study examines various perspectives, including the modeling and presentation of the research. This involves identifying the validation methods, time period for implementation, solver, program, software, toolbox, and platform structures used during validation. Additionally, the characteristics of the database used for implementation and the location of the implementation are detailed. The publication year and the countries of the authors are also provided. Comparative relationships between these elements are presented based on the aforementioned information.

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Section: Republic Of Koreamentioning

confidence: 99%

A Survey on Direct Load Control Technologies in the Smart Grid

Ozkop

2024

IEEE Access

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“…What this datasets provides, other than energy consumption, indoor temperature and weather, is HVAC information, highlighting the setpoint established by the users which we have not found in other studies. However, several research works have shown interest in this kind of data in order to improve demand response in smart buildings 10 , 11 . Apart from that, CO 2 and presence data gathered via a motion sensor is also presented.…”

Section: Background and Summarymentioning

confidence: 99%

PLEIAData: consumption, HVAC, temperature, weather and motion sensor data for smart buildings applications

2023

Self Cite

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The current cost that energy represents is crucial in a field like climate control which has high energy demands, therefore its reduction must be prioritized. The expansion of ICT and IoT come with an extensive deployment of sensors and computation infrastructure creating an opportunity to analyze and optimize energy management. Data on building internal and external conditions is essential for developing efficient control strategies in order to minimize energy consumption while maintaining users’ comfort inside. We here present a dataset that provides key features that could be useful for a wide range of applications in the context of modeling temperature and consumption via Artificial Intelligence algorithms. The data gathering has taken place for almost 1 year in the Pleiades building of the University of Murcia, which is a pilot building of the European project PHOENIX aiming to improve building energy efficiency.

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“…To ensure a stable electricity supply, utilities must invest in expanding their power capacity [3]. However, there are potential financial inefficiencies associated with this solution since it could result in underutilization of capacity during non-peak periods [4]. DR has emerged as a promising solution to balance supply and consumption by focusing on demand-side management [5].…”

Section: Introductionmentioning

confidence: 99%

“…The results of a decision tree analysis to assess the impact of features on override rate highlighted the necessity of considering the practices and preferences of individual households to increase the success of DLC. In 2022, Tomat et al used real-world data to analyze the user interaction with smart thermostats around DLC events [4]. They utilized clustering techniques to group users based on their cooling setpoint (SP) adjustments during DLC events and subsequently simulated their behavior to estimate the corresponding power reduction.…”

Section: Introductionmentioning

confidence: 99%

Exploring thermostat override behavior during direct load control events

Khorasani Zadeh,

Ouf,

Gunay

et al. 2023

J. Phys.: Conf. Ser.

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Direct load control (DLC) is considered a viable solution to promote demand-side energy management, in which the utility provider adjusts consumers’ temperature setpoints via smart thermostats. Users commonly have the option to interrupt DLC and override them by adjusting their thermostat setpoints. However, the occurrence of overrides can have a detrimental impact on the overall efficacy of DLC. The user discomfort and the fact that an override may increase the load unexpectedly on the grid highlight the importance of understanding override mechanisms during DLC and the uncertainty related to occupants’ responses. This study examined user interactions with smart thermostats during DLC events using real-world data from the Ecobee Donate Your Data (DYD) program. According to the results, 35% of DLC was overridden by users, resulting in higher energy consumption during peak periods. A comprehensive analysis of four types of variables was conducted. A decision tree algorithm was used to classify smart thermostat users into two categories: “compliant users,” who rarely override DLC, and “non-compliant users,” who frequently override DLC, based on general information about their behavior and preferences and without any prior DLC experience. Moreover, three distinct types of DLC participants, characterized by their preferences and behaviors, were identified using a clustering algorithm. Classification results provide utilities with insight into where resources and efforts should be allocated to users who are more likely to comply with DLC. Clustering users into different typologies will enable utilities to design targeted and less disruptive DLC better aligned with the needs of DLC participants.

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Understanding patterns of thermostat overrides after demand response events

Cited by 12 publications

References 42 publications

A Survey on Direct Load Control Technologies in the Smart Grid

A Survey on Direct Load Control Technologies in the Smart Grid

PLEIAData: consumption, HVAC, temperature, weather and motion sensor data for smart buildings applications

Exploring thermostat override behavior during direct load control events

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