Data-Driven Predictions of Heating Energy Savings in Residential Buildings

Data-Driven Predictions of Heating Energy Savings in Residential Buildings

Along with the increasing use of intermittent electricity sources, such as wind and sun, comes a growing demand for user flexibility. This has paved the way for a new market of services that provide electricity customers with energy saving solutions. These include a variety of techniques ranging fro...

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Bibliographic Details
Main Authors: Lindblom, Ellen, Almquist, Isabelle
Format: Others
Language:English
Published: Uppsala universitet, Byggteknik 2019
Subjects:
Building Energy
Machine Learning
Energy Savings
Heating Energy
Indoor Temperature
Neural Networks
Support Vector Regression
Random Forest
Ridge Regression
K-Nearest Neighbors
Energy Systems
Energisystem
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387395

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387395

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