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EVIDENT news and events

Research Spotlight presentation

September 16, 2021
TCD presentation at Centre for Innovative Human Systems, in  the ongoing Research Spotlight series.

EVIDENT blog material

September 14, 2021
DUTH blogs about EVIDENT (report in Greek) https://www.xronos.gr/oikonomia/nees-draseis-toy-dpth-gia-tin-enishysi-tis-energeiakis-apotelesmatikotitas  

Evident kick-off

January 7, 2021
The kick-off meeting of the EU-funded H2020 project #EVIDENT was held virtually on 15-16 December, 2020. 9 project partners from 5 countries will investigate the…

Goal

EVIDENT intends to provide new insights in the energy efficiency policy innervations. EVIDENT results will be used to evaluate and propose energy efficiency policy measures that will reduce energy consumption and boost energy efficient technology diffusion.

Objectives

Objective 1

To create a framework for assessing the role of behavioral insights in energy efficiency using a wide range of case studies, experiments, surveys, RCTs in conjunction with state of the art econometric methods and big data analytics.

Objective 3

To enable future research related to behavioral biases and heuristics in energy efficiency by creating through the platform a methodology  and data hub.

Objective 5

To enable wide communication and scientific dissemination of the innovative results to the industry, energy communities, and policy actors.

Objective 2

To develop a platform for raising energy efficiency awareness and support better decision making and policy implementation.

Objective 4

To contribute to energy efficiency policy implementation by evaluating and proposing specific policy interventions to enhance energy efficiency. Also, to design and demonstrate five large scale pilots across Europe, in the energy efficient systems.

Objective 6

To design an innovative business model and conduct a cost benefit techno-economic analysis to strengthen the role of behavioral insights in energy efficiency policy interventions.

EVIDENT PLATFORM

EVIDENT envisions the development of an advanced ecosystem to deliver analytics and provide insights regarding energy efficiency. The platform will enable users to create and answer questionnaires, take part in experiments, or create their own, and participate in a serious game. It will also leverage data visualization tools in order to reveal data patterns, trends, and outliers while at the same time it will make sense to users without technical knowledge. Advanced analytic methodologies, from the domain of econometrics and machine learning as well as business intelligence practices would provide in depth analysis and help the user extract hidden knowledge and perform better decisions. In addition, another innovative feature of the platform will be the development of online tools to address the research that is related with questionnaires and experiments. 

Use case 1: Estimate the importance of consumption feedback in residential users.

Using data from Distribution System Operators that participate in the EVIDENT project we will create historical data on energy consumption for a treatment and a control group and then, the treatment group will be informed about this data in a simplified manner. Results are expected to show that direct power feedback provided by an In-Home Display actually encourages people to make more efficient use of energy and that can reduce consumption of energy, on average, by about 7%. We expect that more than 10,000 consumers will be included in this trial.

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Use case 2: Estimate the relative effectiveness of interventions like peer comparison feedback.

In the second use case we will estimate the impact of provided information regarding the comparisons of a household’s energy use with that of similar neighbours and we will provide energy conservation tips through Home Energy Reports. Generally, perceiving what other people do either directly or indirectly, through information given by a third party, has a powerful influence on agents behaviour. We expect that the “descriptive norm” element of the Home Energy Report treatment, to cause households that previously used more than the norm to decrease usage, but also cause households that used less than the norm to use more.

Use case 3: Investigate the role of big data in assessing the impact of behavioural insights in energy.

Real time energy consumption data will be collected at a disaggregated level in the apartment buildings as well as the adjacent EV chargers. Socioeconomic and demographic variables will be collected through public census data and a survey will estimate the existence of specific behavioral biases regarding energy consumption. The goal is to estimate consumer propensities, by using a logistic regression model over the set of socioeconomic, demographic, and behavioral variables. Statistical results will show whether housing type, number of inhabitants, age, and end use behavior are strong predictors for choosing energy efficient appliances and building energy retrofit investments. 

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Use case 4: Examine the relation of energy consumption behavioural biases with consumers’ financial literacy level.

We expect that low levels of financial literacy may deter consumers from buying more energy-efficient products. A serious game will be designed based on the behavioural biases that are identified in the literature as being most troublesome for energy consumption. A focus group will be convened with a purposeful sample – taking people from various socio-economic groups. The focus group will explore the understanding of the participants of the financial aspects and how it influences their behaviour. 

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Use case 5: Investigate and exploit energy demand curves.

Using experimental approaches, we can allow for different choices to be varied across consumers, by mapping the demand curves for energy efficient devices.  In these experiments, each surveyed customer will be presented with several hypothetical offers by energy suppliers and will be asked to identify the offer that he/she would choose and other attributes that consumers face in real life scenarios. These experiments have the advantage of being able to include service attributes that have not been offered in real world markets or have not varied sufficiently in markets to allow estimation. Surveys are expected to be conducted in several countries using a large number of participants for maximizing estimation of consumers’ heterogeneity. 

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957117. The information contained in this website reflects only the authors’ view. EC is not responsible for any use that may be made of this information.