1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Μεταδιδακτορικοί ερευνητές

Τύπος:	Κεφάλαιο σε πρακτικά συνεδρίου
Τίτλος:	Exploring energy poverty indicators through artificial neural networks
Συγγραφέας:	[EL] Παπαδά, Λευκοθέα[EN] Papada, Lefkothea [EL] Καλιαμπάκος, Δημήτριος[EN] Kaliampakos, Dimitrios
Ημερομηνία:	16/11/2022
Περίληψη:	The present paper is an important step in the development of energy poverty research, introducing artificial intelligence to the analysis of the problem. Literature has shown that conventional mathematical / statistical tools fail to take into account the complexity of different human responses to the energy problem. This weakness is attempted to be overcome with the use of Artifi-cial Neural Networks (ANNs), through the case of Greece. For the purposes of the research, a neural network, i.e. Multilayer Perceptron, of the Machine Learning application/tool “WEKA” was used and trained, in order to predict "objective" energy poverty based on "subjective" aspects. More precisely, three typical objective indicators of energy poverty were selected as output variables, namely, 10%_actual (based on actual expenses), 10%_required (based on required expenses) and CEN (Compression of Energy Needs), and five different subjective indicators were selected as input variables. The anal-ysis showed that certain human behaviors/subjective indicators can predict objective energy poverty at a marginally satisfactory level, in the order of 56%-58%. From the variety of human behaviors and responses, the re-striction of other essentials in order to meet heating needs proves to be the key parameter of predicting energy poverty based on the indicator 10%_actual, while the condition of living in an inadequately heated home emerges as the key parameter reflecting energy poverty based on the CEN indicator. Artificial intelligence is expected to be a promising tool in under-standing energy poverty and, therefore, in planning effective energy poverty policies.
Γλώσσα:	Αγγλικά
Σελίδες:	12
DOI:	10.1007/978-981-19-1653-3_18
ISBN:	978-981-19-1652-6
Θεματική κατηγορία:	[EL] Ενέργεια και Καύσιμα[EN] Energy and Fuels
Λέξεις-κλειδιά:	energy poverty; artificial neural network; objective indicators; subjective indicators; Greece
Κάτοχος πνευματικών δικαιωμάτων:	© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://link.springer.com/chapter/10.1007/978-981-19-1653-3_18
Ηλεκτρονική διεύθυνση περιοδικού:	https://link.springer.com/book/10.1007/978-981-19-1653-3
Τίτλος πηγής δημοσίευσης:	Artificial Intelligence and Sustainable Computing. Algorithms for Intelligent Systems.
Κεφάλαιο:	17
Σελίδες τεκμηρίου (στην πηγή):	231-242
Σειρά δημοσίευσης:	Algorithms for Intelligent Systems (AIS)
Όνομα εκδήλωσης:	3rd International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology ICSISCET 2021
Τοποθεσία εκδήλωσης:	Gwalior, India
Ημ/νία έναρξης εκδήλωσης:	13/11/2021
Ημ/νία λήξης εκδήλωσης:	14/11/2021
Σημειώσεις:	This research is co-financed by Greece and the European union (European Social Fund-ESF) through the operational program «Human Resources Development, Education and Lifelong Learning» in the context of the project “Reinforcement of Postdoctoral Researchers—2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ).
Εμφανίζεται στις συλλογές:	Μεταδιδακτορικοί ερευνητές