Difference between revisions of "Projects:2019s2-25202 Electricity Consumers' Greenness: A Measure to Quantify Electricity Consumers' Sustainability and Efficiency"
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[[Category:Final Year Projects]] | [[Category:Final Year Projects]] | ||
[[Category:2019s2|25202]] | [[Category:2019s2|25202]] | ||
| − | + | Generation and demand balance is a critical job for the grid operator. This balance is being affected by the increasing number of frequency events on the grid which in turn require fast balancing resources. However, existing balancing resources, such as synchronous generators, are retiring for many economic and environmental reasons. Adding more intermittent renewable generation makes the real-time operation a lot more difficult compared to traditional grid, also requiring higher amount of balancing resources. Therefore, this project focuses on introducing a better way of energy consumption by quantifying the inefficiencies in the consumer behaviour. | |
== Introduction == | == Introduction == | ||
Quantifying the energy-efficiency of a building using electricity consumption data has been a research question for many years. These efforts were based on identifying the source of inefficiencies in the building structure or the device itself. However, even in a highly efficient building, consumers with their unique behaviours can degrade the efficiency of energy usage. One example is using an appliance at on-peak hours or setting the HVAC setpoint to an unreasonable level during the same time. As the Internet-of-Things (IoT) is becoming widespread, more information about individual appliance's consumption is available. Therefore, it is time to pinpoint the inefficiency in the consumers' behaviours. In this project, we intend to introduce the "Greenness" factor for a household by first designing a "Greenness" map for every appliance in a household based on their physical and operational limitations, and relevant exogenous factors such as ambient temperature, type of day, the hour of the day, season, etc. Then, we quantify the "Greenness" of a household electricity consumption according to the data for every appliance. In the next step, we develop an aggregate "Greenness" map for every day of every year of the historical data to represent the overall efficiency of the consumer's behaviour in a household. Finally, we convert the aggregate "Greenness" map to a single "Greenness" factor for the household. | Quantifying the energy-efficiency of a building using electricity consumption data has been a research question for many years. These efforts were based on identifying the source of inefficiencies in the building structure or the device itself. However, even in a highly efficient building, consumers with their unique behaviours can degrade the efficiency of energy usage. One example is using an appliance at on-peak hours or setting the HVAC setpoint to an unreasonable level during the same time. As the Internet-of-Things (IoT) is becoming widespread, more information about individual appliance's consumption is available. Therefore, it is time to pinpoint the inefficiency in the consumers' behaviours. In this project, we intend to introduce the "Greenness" factor for a household by first designing a "Greenness" map for every appliance in a household based on their physical and operational limitations, and relevant exogenous factors such as ambient temperature, type of day, the hour of the day, season, etc. Then, we quantify the "Greenness" of a household electricity consumption according to the data for every appliance. In the next step, we develop an aggregate "Greenness" map for every day of every year of the historical data to represent the overall efficiency of the consumer's behaviour in a household. Finally, we convert the aggregate "Greenness" map to a single "Greenness" factor for the household. | ||
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=== Objectives === | === Objectives === | ||
| − | Design a "Greenness" map for every major appliance in a household based on their physical and operational limitations | + | 1. Design a "Greenness" map for every major appliance in a household based on their physical and operational limitations. |
| − | Account for the exogenous factors such as ambient temperature, type of day, the hour of the day, season, etc. when needed | + | 2. Account for the exogenous factors such as ambient temperature, type of day, the hour of the day, season, etc. when needed. |
| − | Aggregating the "Greenness" map for every day of every year of the historical data to represent the overall efficiency of the consumers’ behaviour in a household. | + | 3. Aggregating the "Greenness" map for every day of every year of the historical data to represent the overall efficiency of the consumers’ behaviour in a household. |
Revision as of 21:18, 1 October 2019
Generation and demand balance is a critical job for the grid operator. This balance is being affected by the increasing number of frequency events on the grid which in turn require fast balancing resources. However, existing balancing resources, such as synchronous generators, are retiring for many economic and environmental reasons. Adding more intermittent renewable generation makes the real-time operation a lot more difficult compared to traditional grid, also requiring higher amount of balancing resources. Therefore, this project focuses on introducing a better way of energy consumption by quantifying the inefficiencies in the consumer behaviour.
Contents
Introduction
Quantifying the energy-efficiency of a building using electricity consumption data has been a research question for many years. These efforts were based on identifying the source of inefficiencies in the building structure or the device itself. However, even in a highly efficient building, consumers with their unique behaviours can degrade the efficiency of energy usage. One example is using an appliance at on-peak hours or setting the HVAC setpoint to an unreasonable level during the same time. As the Internet-of-Things (IoT) is becoming widespread, more information about individual appliance's consumption is available. Therefore, it is time to pinpoint the inefficiency in the consumers' behaviours. In this project, we intend to introduce the "Greenness" factor for a household by first designing a "Greenness" map for every appliance in a household based on their physical and operational limitations, and relevant exogenous factors such as ambient temperature, type of day, the hour of the day, season, etc. Then, we quantify the "Greenness" of a household electricity consumption according to the data for every appliance. In the next step, we develop an aggregate "Greenness" map for every day of every year of the historical data to represent the overall efficiency of the consumer's behaviour in a household. Finally, we convert the aggregate "Greenness" map to a single "Greenness" factor for the household.
Project team
Project students
- Usman Khan
- Farrukh Ahsan
- Bo Yin
Supervisors
- Ali Pourmousavi Kani
Advisors
- Associate Professor Nesimi Ertugrul
Objectives
1. Design a "Greenness" map for every major appliance in a household based on their physical and operational limitations.
2. Account for the exogenous factors such as ambient temperature, type of day, the hour of the day, season, etc. when needed.
3. Aggregating the "Greenness" map for every day of every year of the historical data to represent the overall efficiency of the consumers’ behaviour in a household.
Background
Topic 1
Method
Results
Conclusion
References
[1] McNeill, D, Wilkie, W, Public Policy and Consumer Information: Impact of the New Energy Labels, Journal of Consumer Research, vol. 6, 1979
[2] Murphy, L, The influence of energy audits on the energy efficiency investments of private owner-occupied households in the Netherlands, ScienceDirect, 2014
[3] Source: Pecan Street Inc. Dataport, 2019
[4] Kussel, G, Frondel, M, Switching on electricity demand response: Evidence for German households, EconPapers, 2016
