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dc.contributor.author
Tsalikidis, Nikolaos
en
dc.date.accessioned
2023-04-05T08:49:52Z
dc.date.available
2023-04-05T08:49:52Z
dc.date.issued
2023-04-05
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/30134
dc.rights
Default License
dc.subject
Energy load forecasting
en
dc.subject
Machine learning
en
dc.subject
Time series forecasting
el
dc.subject
Ensemble methods
el
dc.subject
Smart building
el
dc.title
Data mining/ Machine Learning for Smart House in-frastructure
en
heal.type
masterThesis
en_US
heal.dateAvailable
2023-03-13
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2023-03-13
heal.abstract
This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. The recent exponential growth of available data in today’s fast-paced technology-driven world has made systematic data collection and the concurrent extraction of meaningful information through them a necessity. In light of the adverse effects of climate change, data analysis and AI techniques can be directly applied to provide forecasts that could be used for the efficient scheduling and operation of energy usage. Especially in the built environment, Energy Load Forecasting (ELF) enables Distribution System Operators (DSO) or Aggregators to accurately predict the energy demand and generation tradeoffs. Today’s near-zero Energy buildings (nZEBs), which are buildings with very high energy performance, have become the standard in Europe in terms of building performance from 2021 onwards and benefiting from the recent Internet of Things (IoT) technological advances, they are equipped with on-site smart monitoring systems and services, advanced communication technologies. The growing amount of data produced by such IoT applications has empowered decision-making via data analytics techniques. This dissertation focuses on the development and comparison of predictive algorithms for a state-of-the-art nZEB smart building and metered data produced by its sensors and advanced monitoring systems. In particular, this involves energy load consumption, as well as weather data which are used to develop, train, and evaluate Machine Learning (ML) models. Various ML algorithms and methodologies, as well as combinations thereof, are explored and put to the test, each with its unique characteristics, in order to produce a robust approach for One-Step-Ahead Energy Load Forecasting (OSA-ELF). The effect of exogenous parameters is assessed, and comparisons are made between the different methods tested on both variating portions of the training dataset but also on new unseen data with similar properties and characteristics.
el
heal.advisorName
Tjortjis, Christos
el
heal.committeeMemberName
Koukaras, Paraskeuas
en
heal.committeeMemberName
Ioannidis, Dimosthenis
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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