Machine Learning Models for Electricity Consumption Forecasting: A Review

Alfonso González-Briones; Guillermo Hernandez; Juan M. Corchado; Sigeru Omatu; Mohd Saberi Mohamad

doi:10.1109/CAIS.2019.8769508

Machine Learning Models for Electricity Consumption Forecasting: A Review

Alfonso González-Briones
, Guillermo Hernandez
, Juan M. Corchado
, Sigeru Omatu
, Mohd Saberi Mohamad

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

74 Citations (Scopus)

Abstract

The prediction of energy consumption is a task that allows energy supply companies to adapt to certain behaviors. Among these activities that companies can perform is to know the behavior of their customers to adapt their rates to consumption or know the intervals in which it will produce a greater demand for energy and have planned the adaptation of supply chains. In this sense, it is necessary to carry out an evaluation of methods that allow forecasting future energy consumption based on the consumption history and other variables of the users themselves. In this article, a review of the main machine learning models that allow predicting energy consumption using a one-year data set of a shoe store was made. The review made allowed to observe that for the data set using the Linear Regression and Support Vector Regression has obtained a success of 85.7% being the best results provided.

Original language	English
Title of host publication	2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728101088
DOIs	https://doi.org/10.1109/CAIS.2019.8769508
Publication status	Published - May 2019
Externally published	Yes
Event	2nd International Conference on Computer Applications and Information Security, ICCAIS 2019 - Riyadh, Saudi Arabia Duration: May 1 2019 → May 3 2019

Publication series

Name	2nd International Conference on Computer Applications and Information Security, ICCAIS 2019

Conference

Conference	2nd International Conference on Computer Applications and Information Security, ICCAIS 2019
Country/Territory	Saudi Arabia
City	Riyadh
Period	5/1/19 → 5/3/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Decision Tree
Energy Forecasting
K-nearest Neighbours
Linear Regression
Machine Learning
Random Forest
Support Vector Regression

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Information Systems and Management
Health Informatics
Information Systems
Safety, Risk, Reliability and Quality
Artificial Intelligence

Access to Document

10.1109/CAIS.2019.8769508

Cite this

González-Briones, A., Hernandez, G., Corchado, J. M., Omatu, S., & Mohamad, M. S. (2019). Machine Learning Models for Electricity Consumption Forecasting: A Review. In 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019 Article 8769508 (2nd International Conference on Computer Applications and Information Security, ICCAIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAIS.2019.8769508

Machine Learning Models for Electricity Consumption Forecasting: A Review. / González-Briones, Alfonso; Hernandez, Guillermo; Corchado, Juan M. et al.
2nd International Conference on Computer Applications and Information Security, ICCAIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8769508 (2nd International Conference on Computer Applications and Information Security, ICCAIS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

González-Briones, A, Hernandez, G, Corchado, JM, Omatu, S & Mohamad, MS 2019, Machine Learning Models for Electricity Consumption Forecasting: A Review. in 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019., 8769508, 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019, Riyadh, Saudi Arabia, 5/1/19. https://doi.org/10.1109/CAIS.2019.8769508

González-Briones A, Hernandez G, Corchado JM, Omatu S, Mohamad MS. Machine Learning Models for Electricity Consumption Forecasting: A Review. In 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8769508. (2nd International Conference on Computer Applications and Information Security, ICCAIS 2019). doi: 10.1109/CAIS.2019.8769508

González-Briones, Alfonso ; Hernandez, Guillermo ; Corchado, Juan M. et al. / Machine Learning Models for Electricity Consumption Forecasting : A Review. 2nd International Conference on Computer Applications and Information Security, ICCAIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2nd International Conference on Computer Applications and Information Security, ICCAIS 2019).

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keywords = "Decision Tree, Energy Forecasting, K-nearest Neighbours, Linear Regression, Machine Learning, Random Forest, Support Vector Regression",

author = "Alfonso Gonz{\'a}lez-Briones and Guillermo Hernandez and Corchado, \{Juan M.\} and Sigeru Omatu and Mohamad, \{Mohd Saberi\}",

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PY - 2019/5

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AB - The prediction of energy consumption is a task that allows energy supply companies to adapt to certain behaviors. Among these activities that companies can perform is to know the behavior of their customers to adapt their rates to consumption or know the intervals in which it will produce a greater demand for energy and have planned the adaptation of supply chains. In this sense, it is necessary to carry out an evaluation of methods that allow forecasting future energy consumption based on the consumption history and other variables of the users themselves. In this article, a review of the main machine learning models that allow predicting energy consumption using a one-year data set of a shoe store was made. The review made allowed to observe that for the data set using the Linear Regression and Support Vector Regression has obtained a success of 85.7% being the best results provided.

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Machine Learning Models for Electricity Consumption Forecasting: A Review

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this