Informing the Development of Domestic Energy Efficiency Initiatives

Parnell, Rosie; Larsen, Olga Popovic

doi:10.1177/0013916504274008

Cited by 41 publications

(49 citation statements)

References 39 publications

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“…saving money, environmental or sustainability concerns, etc.) and to construct relevant messages to consumers [28]. How the electricity industry engages with their residential customers to affect peak energy demand has potentially important direct and indirect influence on consumer demand and therefore behaviour.…”

Section: Customer-industry Engagement (Cie)mentioning

confidence: 99%

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

Vine¹,

Buys²,

Lewis³

et al. 2016

OJEE

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This paper explores the importance of customer-industry engagement (CIE) to peak energy demand by means of a newly developed Bayesian Network (BN) complex systems model entitled the Residential Electricity Peak Demand Model (REPDM). The REPDM is based on a multi-disciplinary perspective designed to solve the complex problem of residential peak energy demand. The model provides a way to conceptualise and understand the factors that shift and reduce consumer demand in peak times. To gain insight into the importance of customer-industry engagement in affecting residential peak demand, this research investigates intervention impacts and major influences through testing five scenarios using different levels of customer-industry engagement activities. Scenario testing of the model outlines the dependencies between the customer-industry engagement interventions and the probabilities that are estimated to govern the dependencies that influence peak demand. The output from the model shows that there can be a strong interaction between the level of CIE activities and interventions. The influence of CIE activity can increase public and householder support for peak reduction and the model shows how the economic, technical and social interventions can achieve greater peak demand reductions when well-designed with appropriate levels of CIE activities.

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Section: Customer-industry Engagement (Cie)mentioning

confidence: 99%

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

Vine¹,

Buys²,

Lewis³

et al. 2016

OJEE

View full text Add to dashboard Cite

show abstract

“…An optimum/effective method should be practiced that matches energy efficiency -including users' comfort and environmental issues -, users' behavior and building design. Energy efficiency specialists should first understand users' behavior (Parnell & Popovics-Larsen, 2005) as often building energy efficiency practices do not answer users' demands and they should take realities into account which limit the success of efficiency measures (Heiskanen & Lovio, 2010). Firstly, it is to be stated that energy use is the result of other purposes rather than simply the act of energy use.…”

Section: Impact Of Energy Users' Behaviormentioning

confidence: 99%

Building a School Energy Policy – A Review Paper

Korkut

Llaci²

2016

JESR

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Human has always searched for higher living standards and greater power. Higher Significance of School Energy PolicyEnergy policy is a means of determining how energy will be produced, contributed and consumed. It is derived from energy conservation guidelines, public concerns, state legislation, taxation, international agreements, environmental issues and investment encouragement. U.S. Department of Energy defines energy policy as "An energy policy establishes and validates top management's commitment to energy performance improvement. It must be defined by top management, as well as documented and understood by employees and anyone working on behalf of the organization" (U.S. Department of Energy, 2015).Although The European Union's interest in energy efficiency and policy started with energy crisis due to dramatically increased oil prices in the 70s with some poorly implemented energy directives, in 1989 the first uses of The TERMIE program that provided energy technologies was a milestone and the original SAVE program was approved in 1991 (Allard & Seppanen, 2009). The 1998's action plan recommended the Union Members 1% additional energy efficiency improvement higher than expectations. The European Union announced its first mandatory energy policy in 2005 followed by "Energy for a Changing World" policy in January 2007 and EU obligatory 20/20/20 energy policy objectives in March 2007(Obrecht et al., 2013. The leaders of the Union agreed in March 2007 to reduce the EU's projected energy consumption by 20 % by 2020. That is the equivalent of closing 400 power stations. In 2011, the Union proposed an energy efficiency plan up to the year 2050 and in 2012, an energy efficiency directive for indicative energy efficiency targets (EUR-Lex, 2015). In October 2014, the Union leaders agreed on a higher energy savings target of 27 %, or greater, by 2030.On a national scale -depending on the country in accordance with the constitution, relevant directives and international treaties-a national energy policy basically aims at:• Increasing energy efficiency, energy reliability and job opportunities

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“…In the context of giving energy advice, literature shows that prior domain knowledge and commitment to save energy are important factors that influence the intention to follow advice [7,22]. To evaluate our system, the intention to follow recommendations is an important indicator of usefulness, in addition to factors relating to the predicted information, such as confidence and intelligibility, as well as potential issues with privacy.…”

Section: Recommender Systems and Energymentioning

confidence: 99%

Recommending energy tariffs and load shifting based on smart household usage profiling

Fischer

Ramchurn

Osborne

et al. 2013

Proceedings of the 2013 International Conference on Intelligent User Interfaces

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We present a system and study of personalized energyrelated recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.

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Informing the Development of Domestic Energy Efficiency Initiatives

Cited by 41 publications

References 39 publications

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

Application of a Bayesian Network Complex System Model Examining the Importance of Customer-Industry Engagement to Peak Electricity Demand Reduction

Building a School Energy Policy – A Review Paper

Recommending energy tariffs and load shifting based on smart household usage profiling

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