Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Using Opaque AI for Smart Grids

Lundberg, Jesper LU and Lundborg, Alexander LU (2020) SYSK16 20201
Department of Informatics
Abstract
Deep learning is an emerging machine learning technique which can find complex patterns in large amounts of data. This makes it useful for several applications in smart grids, which often involve the processing of large amounts of data. However, there are reasons to be sceptical of its suitability as the black-box nature of deep learning could be a problem since power grids are important infrastructure and contain deadly currents. Professionals in smart grids were interviewed to provide an understanding of the importance of eight issues relating to the interpretability of machine learning. The findings show that for some uses related to controlling the grid, trust is of critical importance, and it is unlikely that a black-box algorithm... (More)
Deep learning is an emerging machine learning technique which can find complex patterns in large amounts of data. This makes it useful for several applications in smart grids, which often involve the processing of large amounts of data. However, there are reasons to be sceptical of its suitability as the black-box nature of deep learning could be a problem since power grids are important infrastructure and contain deadly currents. Professionals in smart grids were interviewed to provide an understanding of the importance of eight issues relating to the interpretability of machine learning. The findings show that for some uses related to controlling the grid, trust is of critical importance, and it is unlikely that a black-box algorithm will be used. For other uses such as giving recommendations and forecasts, it was found that either trust or informativeness is required for the results to be useful, although trust could potentially be achieved through a strong track-record, rather than through the ability to interpret the system. Other issues were of varying importance, but none of them critical. Unless the area of interpretability sees considerable progress, it will be of concern when creating deep learning systems for smart grids. (Less)
Please use this url to cite or link to this publication:
author
Lundberg, Jesper LU and Lundborg, Alexander LU
supervisor
organization
alternative title
How does the difficulty of interpreting deep learning systems affect their suitability for smart grids?
course
SYSK16 20201
year
type
M2 - Bachelor Degree
subject
keywords
Deep Learning, Smart Grids, Interpretability, Black-Box, Transparency, Post- hoc Explainability, AI, Machine Learning
report number
INF20-01
language
English
id
9016702
date added to LUP
2020-06-26 14:06:14
date last changed
2020-06-26 14:06:14
@misc{9016702,
  abstract     = {{Deep learning is an emerging machine learning technique which can find complex patterns in large amounts of data. This makes it useful for several applications in smart grids, which often involve the processing of large amounts of data. However, there are reasons to be sceptical of its suitability as the black-box nature of deep learning could be a problem since power grids are important infrastructure and contain deadly currents. Professionals in smart grids were interviewed to provide an understanding of the importance of eight issues relating to the interpretability of machine learning. The findings show that for some uses related to controlling the grid, trust is of critical importance, and it is unlikely that a black-box algorithm will be used. For other uses such as giving recommendations and forecasts, it was found that either trust or informativeness is required for the results to be useful, although trust could potentially be achieved through a strong track-record, rather than through the ability to interpret the system. Other issues were of varying importance, but none of them critical. Unless the area of interpretability sees considerable progress, it will be of concern when creating deep learning systems for smart grids.}},
  author       = {{Lundberg, Jesper and Lundborg, Alexander}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Using Opaque AI for Smart Grids}},
  year         = {{2020}},
}