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Mette Herrem

dblp:266/3658 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Energy systems and smart grids · 100%
Artificial intelligence
1 paper
Time series and sequential data · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Time series and sequential data
ensemble forecasting		 0.112020
Day-Ahead Forecasting of Losses in the Distribution Network · AAAI 2020

Methods — techniques the papers use, named apart from their topics

machine learning · 0.9ensemble model selection · 0.9
YearPublicationVenuePosition
2020 Day-Ahead Forecasting of Losses in the Distribution Network
abstract
We present a commercially deployed machine learning system that automates the day-ahead nomination of the expected grid loss for a Norwegian utility company. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduces the MAE with 41% from 3.68 MW to 2.17 MW per hour from mid July to mid October. It is robust and reduces manual work.
Nisha Dalal, Martin Mølnå, Mette Herrem, Magne Røen, Odd Erik Gundersen
 AAAI3