Sustainability impact assessment of an intelligent control system for residential heating

Bracquené, Ellen; Bock, Yannick De; Duflou, Joost R.

doi:10.1016/j.procir.2020.02.007

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Ref. [33], for example, present an intelligent control system that takes into account the expected occupancy in order to adapt the thermostat and save energy. They do not take into account learning the occupancy model, but take into account the LCA of the smart thermostats and show that the energy needed for these devices across their whole life cycle will almost always be lower than the energy saved.…”

Section: Case Studiesmentioning

confidence: 99%

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

et al. 2022

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In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green.

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Section: Case Studiesmentioning

confidence: 99%

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

et al. 2022

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“…Life cycle analysis and life cycle costing methods can be used successfully, for example, when planning residential heating systems. The article [16] describes the use of small thermostats that will save energy without reducing the user comfort of living. The study found that heat pumps are now often installed in new well insulated homes and are considered an environmentally friendly alternative to fossil fueled heating systems.…”

Section: Life Cycle Assessment (Lca)mentioning

confidence: 99%

Residential Construction with a Focus on Evaluation of the Life Cycle of Buildings

et al. 2021

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The article focuses on highlighting the role of life cycle costing (LCC) in the preparatory and implementation phase of residential projects. It involves the evaluation of several investment scenarios in the pre-investment phase, the choice between variants of the design of the entire building or its parts, and the choice of variants of structures and equipment with acceptable parameters. An innovative method of evaluating the life cycle of buildings is described in the article. This method was tested in selected residential projects realized by Skanska in the Czech Republic. Experience from construction practice shows that the choice of variants, constructions, or equipment of buildings only on the basis of the lowest acquisition costs (lowest bid prices) is wrong. The LCC calculation tool has been designed to model life cycle costs of individual variants of construction designs with different input parameters. It is possible to analyze the components or equipment that have the greatest impact on total life cycle costs. The article presents a tool that evaluates the long-term economic efficiency of the proposed residential buildings in terms of analysis of life cycle costs. The article will also expand the knowledge of the professional and general public about the importance of examining investment and operating costs already in the phase of construction preparation.

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“…It can be noted that other papers, which include an evaluation of part of these impacts, can be found in the literature. [8] for example present an intelligent control systems that takes into account the expected occupancy in order to adapt the thermostat and save energy. They do not take into account learning the occupancy model, but take into account the LCA of the smart thermostats, and show that the energy needed for these devices across their whole life cycle will almost always be lower that the energy saved.…”

Section: Case Studiesmentioning

confidence: 99%

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment

Ligozat¹,

Longhi²,

Bugeau³

et al. 2021

Preprint

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In the past ten years artificial intelligence has encountered such dramatic progress that it is seen now as a tool of choice to solve environmental issues and in the first place greenhouse gas emissions (GHG). At the same time the deep learning community began to realize that training models with more and more parameters required a lot of energy and as a consequence GHG emissions. To our knowledge, questioning the complete environmental impacts of AI methods for environment ("AI for green"), and not only GHG, has never been addressed directly. In this article we propose to study the possible negative impact of "AI for green" 1) by reviewing first the different types of AI impacts 2) by presenting the different methodologies used to assess those impacts, in particular life cycle assessment and 3) by discussing how to assess the environmental usefulness of a general AI service.

show abstract

Sustainability impact assessment of an intelligent control system for residential heating

Cited by 4 publications

References 4 publications

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

Residential Construction with a Focus on Evaluation of the Life Cycle of Buildings

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment

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