Projects:2019s2-25202 Electricity Consumers' Greenness: A Measure to Quantify Electricity Consumers' Sustainability and Efficiency

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

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

There has been no previous work done on the estimation of Electricity Consumers’ Greenness. However, there are other areas of research that can be related to Consumers’ Greenness. The following table is a summary of some key research focus areas and their impact on consumer behaviour.

Table 1: Pros and Cons of Research Focus Areas related to Consumers' Greenness

Focus of Study	Pros	Cons
Residential Energy Efficiency Ratings	• Highlight ability of efficient households to satisfy consumers	• Not influential due to lack of consumers’ engagement in relation to sustainability certification and implementation • Static calculation discarding long-term effects
Energy Rating Labels on Appliances	• Communicate useful energy information	• No significant behavioral change
Energy Audit	• Energy based renovation is driven by householder perception of comfort and acceptable outlay on energy bills	• Complex outlay • High payback periods
Demand Response	• Flexible price design • No compromise of consumer privacy	• Electricity demand of uninformed households' price-inelastic • No real-time power
Non-Intrusive Load Monitoring	• Easy to monitor energy consumption • Flexible options for grid operator	• Insufficient point of references for applicability

Most of the research carried out in the field of engineering involves assumption of certain conditions or quantities being constant in order to observe the key parameters being considered. This is usually carried out while formulating equations or cases that serve as the point of reference for other data parameters to be compared and tested against. Similarly, Electricity Consumers’ Greenness requires the formulation of a point of reference against which normal behaviour of major appliances can be compared. This point of reference can be defined as the base case. The base case involves monitoring the consumption data of major appliances in a household during the time in which consumers have negligible interaction with the appliances (e.g. 12 AM to 5 AM). We are assuming that consumers would not be interacting with the household appliances after midnight to early hours of the day. Our main focus here is to consider household appliances that follow a repetitive set cycle throughout the day. Appliances such as heat pumps and refrigerators follow this cyclic pattern of electricity consumption, therefore known as cyclic appliances. We have chosen refrigerators as the key appliance in our project because they consume electricity in an ‘ON/OFF’ duty cycle rather than one continuous cycle of operation. The base case can be formulated once we obtain the electrical consumption data of a refrigerator during hours without consumer interaction. Next step would involve the observation of real consumption data with consumer interaction during the day (peak hours).

Figure 1: Power vs time graph for Base Case/Real Data comparison

Figure 1 is a clear representation of what we primarily intend to achieve in this project. The base case shows a cyclic behaviour of power consumption against time. The real data i.e. the power consumed by the appliance inclusive of consumer behaviour is shown to have differences as compared to the base case. It is important to note that these differences occur for different time widths at the same power level. These differences signify the inefficiencies of the appliance, which can then be quantified further to give us a Greenness factor of the appliance during the day.

Methodology

Modelling approach

Different modelling approaches

Table 2: Summary of Data-driven vs Physics-based approaches

Data-driven	Physics-Based
Accurate approach for nonlinear system networks	System based on state variables represented by linearized state equations
Requires Less knowledge of physical system	Requires extensive knowledge of factors/inputs
Large data is needed to develop an accurate model	Assumptions in linearizing model can lead to errors
Training is time consuming	Actual behaviour might be different from linearized model

shows a summary of the modelling approaches that have been considered for our project. These are data-driven and physics-based respectively.

Data-driven systems treat the system being modelled like a ‘black box’ – a set of training inputs and desired output are defined without any knowledge of the internal workings. This is an accurate approach for non-linear systems as it requires minimum knowledge of the physical aspects of the system. However, large datasets and parameter selections is a tedious and time-taking task in data-driven systems. Data-driven systems involve training through a trial-and-error process to learn and be able to predict from the defined set of data. Physics-based systems employ a mathematical approach, consisting of physical variables represented by linearized state equations. This requires extensive knowledge of the physical system before defining the set of state variables. The linearized model in a physics-based system is based on assumption of some variables which linearize the non-linear systems. This introduces errors in a non-linear, dynamic system. Our project involves numerous unpredictable inputs, such as consumer behavior and exogenous factors, and a targeted (desired) output. A data-driven model best suits our modelling approach to obtain the base case as we prioritize a learning, iterative process in comparison to a linearized, defined model. It is important to develop a sustainable system which learns from data sets and accurately predicts possible situations based on intuition.

Artificial Neural Network (Data Driven model)

Amongst numerous deep learning toolboxes, we have chosen the Artificial Neural Networks (ANN) to serve as our data-driven model. ANN functions like a human mind, consisting of interconnected neurons with a capability to learn from a training data set and predict results accordingly.

The inputs are fed into the input layer which is often a single layer. Larger number of hidden layers would increase the processing time of the neural network to produce an output. The hidden layers contain neurons and perform transformation of the non-linear inputs entered. The output layer is connected to the hidden layer and displays the output of the model.

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