Forecasting Revenue from Electricity Sales of Household Customers using Various Methods

Mehi Zulqaida Harisandy; Diana Purwitasari

doi:10.1109/ICITISEE57756.2022.10057941

Forecasting Revenue from Electricity Sales of Household Customers using Various Methods

Mehi Zulqaida Harisandy, Diana Purwitasari

Institut Teknologi Sepuluh Nopember

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

Abstract

Electricity is one of the most important part in the development of a region. The development of sustainable accompanied by rapid technological advances and an increase in living standards can bring about the increase consumption of electrical energy by customers. Revenue from sales, like revenue from providing services, is recorded when there is an increase in assets as a result of the company's business activities with its customers. The data used are the sales of electrical energy of household customers, both pre-paid and post-paid customers monthly for 5 years back. This paper aims to forecast the revenue from electricity sales of household customers using various method of algorithms, such as k-nearest neighbor, random forest, linear regression, gradient boosting and adaboost. As the result shows that random forest method has the highest R2 value, it increases predicted revenue in 2021 by 21,44% from the actual revenue.

Original language	English
Title of host publication	Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
Subtitle of host publication	Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	508-513
Number of pages	6
ISBN (Electronic)	9798350399615
DOIs	https://doi.org/10.1109/ICITISEE57756.2022.10057941
Publication status	Published - 2022
Event	6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 - Virtual, Online, Indonesia Duration: 13 Dec 2022 → 14 Dec 2022

Publication series

Name	Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

Conference

Conference	6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Country/Territory	Indonesia
City	Virtual, Online
Period	13/12/22 → 14/12/22

Keywords

AdaBoost
Forecasting
Gradient Boosting
K-nearest neighbor
Linear Regression
Random Forest
Revenue

UN SDGs

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

Access to Document

10.1109/ICITISEE57756.2022.10057941

Cite this

Harisandy, M. Z., & Purwitasari, D. (2022). Forecasting Revenue from Electricity Sales of Household Customers using Various Methods. In Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022 (pp. 508-513). (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITISEE57756.2022.10057941

Harisandy, Mehi Zulqaida ; Purwitasari, Diana. / Forecasting Revenue from Electricity Sales of Household Customers using Various Methods. Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 508-513 (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022).

@inproceedings{7c322e76d9b34e24974d230427e0714b,

title = "Forecasting Revenue from Electricity Sales of Household Customers using Various Methods",

abstract = "Electricity is one of the most important part in the development of a region. The development of sustainable accompanied by rapid technological advances and an increase in living standards can bring about the increase consumption of electrical energy by customers. Revenue from sales, like revenue from providing services, is recorded when there is an increase in assets as a result of the company's business activities with its customers. The data used are the sales of electrical energy of household customers, both pre-paid and post-paid customers monthly for 5 years back. This paper aims to forecast the revenue from electricity sales of household customers using various method of algorithms, such as k-nearest neighbor, random forest, linear regression, gradient boosting and adaboost. As the result shows that random forest method has the highest R2 value, it increases predicted revenue in 2021 by 21,44% from the actual revenue.",

keywords = "AdaBoost, Forecasting, Gradient Boosting, K-nearest neighbor, Linear Regression, Random Forest, Revenue",

author = "Harisandy, {Mehi Zulqaida} and Diana Purwitasari",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022 ; Conference date: 13-12-2022 Through 14-12-2022",

year = "2022",

doi = "10.1109/ICITISEE57756.2022.10057941",

language = "English",

series = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "508--513",

booktitle = "Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering",

address = "United States",

}

Harisandy, MZ & Purwitasari, D 2022, Forecasting Revenue from Electricity Sales of Household Customers using Various Methods. in Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022, Institute of Electrical and Electronics Engineers Inc., pp. 508-513, 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022, Virtual, Online, Indonesia, 13/12/22. https://doi.org/10.1109/ICITISEE57756.2022.10057941

Forecasting Revenue from Electricity Sales of Household Customers using Various Methods. / Harisandy, Mehi Zulqaida; Purwitasari, Diana.
Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 508-513 (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022).

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

TY - GEN

T1 - Forecasting Revenue from Electricity Sales of Household Customers using Various Methods

AU - Harisandy, Mehi Zulqaida

AU - Purwitasari, Diana

PY - 2022

Y1 - 2022

N2 - Electricity is one of the most important part in the development of a region. The development of sustainable accompanied by rapid technological advances and an increase in living standards can bring about the increase consumption of electrical energy by customers. Revenue from sales, like revenue from providing services, is recorded when there is an increase in assets as a result of the company's business activities with its customers. The data used are the sales of electrical energy of household customers, both pre-paid and post-paid customers monthly for 5 years back. This paper aims to forecast the revenue from electricity sales of household customers using various method of algorithms, such as k-nearest neighbor, random forest, linear regression, gradient boosting and adaboost. As the result shows that random forest method has the highest R2 value, it increases predicted revenue in 2021 by 21,44% from the actual revenue.

AB - Electricity is one of the most important part in the development of a region. The development of sustainable accompanied by rapid technological advances and an increase in living standards can bring about the increase consumption of electrical energy by customers. Revenue from sales, like revenue from providing services, is recorded when there is an increase in assets as a result of the company's business activities with its customers. The data used are the sales of electrical energy of household customers, both pre-paid and post-paid customers monthly for 5 years back. This paper aims to forecast the revenue from electricity sales of household customers using various method of algorithms, such as k-nearest neighbor, random forest, linear regression, gradient boosting and adaboost. As the result shows that random forest method has the highest R2 value, it increases predicted revenue in 2021 by 21,44% from the actual revenue.

KW - AdaBoost

KW - Forecasting

KW - Gradient Boosting

KW - K-nearest neighbor

KW - Linear Regression

KW - Random Forest

KW - Revenue

UR - http://www.scopus.com/inward/record.url?scp=85150470759&partnerID=8YFLogxK

U2 - 10.1109/ICITISEE57756.2022.10057941

DO - 10.1109/ICITISEE57756.2022.10057941

M3 - Conference contribution

AN - SCOPUS:85150470759

T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022

SP - 508

EP - 513

BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

Y2 - 13 December 2022 through 14 December 2022

ER -

Harisandy MZ, Purwitasari D. Forecasting Revenue from Electricity Sales of Household Customers using Various Methods. In Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 508-513. (Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022). doi: 10.1109/ICITISEE57756.2022.10057941

Forecasting Revenue from Electricity Sales of Household Customers using Various Methods

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this