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Algorithm selection and combining multiple learners for residential energy prediction

Title	Algorithm selection and combining multiple learners for residential energy prediction
Author	Güngör, Onat, Akşanlı, B., Aydoğan, Reyhan
Publication Date:	2019-10
Publication Place	- Elsevier
Subject	Electricity consumption prediction, Algorithm selection, Combining multiple learners, Time series prediction
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0167-739X
Record ID	a1a927e8-ad4d-450a-ab84-1f0abf7cea23
Library Location	Computer Science
Date	2019-10
Sample Text	Balancing supply and demand management in energy grids requires knowing energy consumption in advance. Therefore, forecasting residential energy consumption accurately plays a key role for future energy systems. For this purpose, in the literature a number of prediction algorithms have been used. This work aims to increase the accuracy of those predictions as much as possible. Accordingly, we first introduce an algorithm selection approach, which identifies the best prediction algorithm for the given residence with respect to its characteristics such as number of people living, appliances and so on. In addition to this, we also study combining multiple learners to increase the accuracy of the predictions. In our experimental setup, we evaluate the aforementioned approaches. Empirical results show that adopting an algorithm selection approach performs better than any single prediction algorithm. Furthermore, combining multiple learners increases the accuracy of the energy consumption prediction significantly.
DOI	10.1016/j.future.2019.04.018
Cilt	99

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Özyeğin University

Algorithm selection and combining multiple learners for residential energy prediction

Author Güngör, Onat, Akşanlı, B., Aydoğan, Reyhan

Publication Date 2019-10

Publication Place - Elsevier

Subject Electricity consumption prediction, Algorithm selection, Combining multiple learners, Time series prediction

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0167-739X

Record ID a1a927e8-ad4d-450a-ab84-1f0abf7cea23

Library Location Computer Science

Date 2019-10

Sample Text Balancing supply and demand management in energy grids requires knowing energy consumption in advance. Therefore, forecasting residential energy consumption accurately plays a key role for future energy systems. For this purpose, in the literature a number of prediction algorithms have been used. This work aims to increase the accuracy of those predictions as much as possible. Accordingly, we first introduce an algorithm selection approach, which identifies the best prediction algorithm for the given residence with respect to its characteristics such as number of people living, appliances and so on. In addition to this, we also study combining multiple learners to increase the accuracy of the predictions. In our experimental setup, we evaluate the aforementioned approaches. Empirical results show that adopting an algorithm selection approach performs better than any single prediction algorithm. Furthermore, combining multiple learners increases the accuracy of the energy consumption prediction significantly.

DOI 10.1016/j.future.2019.04.018

Cilt 99