Short Term Electricity Load Forecasting Through Machine Learning

Ahmad Taher Azar; Alaa Khamis; Nashwa Ahmad Kamal; Brian Galli

doi:10.1007/978-3-030-44289-7_40

Short Term Electricity Load Forecasting Through Machine Learning

Ahmad Taher Azar^*
, Alaa Khamis
, Nashwa Ahmad Kamal
, Brian Galli

^*Corresponding author for this work

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

5 Scopus citations

Abstract

Essentially, Electricity Load Forecasting is an approximation of upcoming active loads from a variety of load buses before the active loads occur. Also, it is an important factor for power system energy management. Accurate and precise load forecasting can help to reduce the capacity of the power system, to make unit commitment decisions, and to increase the dependability of power systems. Hence, this paper presents a generalized regression Neural Network (GRNN) based approach for Short Term Load Forecasting (STLF). The results showed that the performance of GRNN with 30 neurons is better of short-term load forecasting in comparison with 10 neurons. For 10 neurons, the Mean Absolute Percentage Error (MAPE) was 2.10% and Mean Absolute Error (MAE) was 306.21 MWh. However, for 30 neurons it was observed that MAPE is 1.81% and MAE 268.48 MWh.

Original language	English
Title of host publication	Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020
Editors	Aboul-Ella Hassanien, Ahmad Taher Azar, Tarek Gaber, Diego Oliva, Fahmy M. Tolba
Publisher	Springer
Pages	427-437
Number of pages	11
ISBN (Print)	9783030442880
DOIs	https://doi.org/10.1007/978-3-030-44289-7_40
State	Published - 2020
Externally published	Yes
Event	1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020 - Cairo, Egypt Duration: 8 Apr 2020 → 10 Apr 2020

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1153 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020
Country/Territory	Egypt
City	Cairo
Period	8/04/20 → 10/04/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Electricity Load Forecasting
Energy management
Power system
Short Term Load Forecasting (STLF)

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-44289-7_40

Cite this

Azar, A. T., Khamis, A., Kamal, N. A., & Galli, B. (2020). Short Term Electricity Load Forecasting Through Machine Learning. In A.-E. Hassanien, A. T. Azar, T. Gaber, D. Oliva, & F. M. Tolba (Eds.), Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020 (pp. 427-437). (Advances in Intelligent Systems and Computing; Vol. 1153 AISC). Springer. https://doi.org/10.1007/978-3-030-44289-7_40

Azar, Ahmad Taher ; Khamis, Alaa ; Kamal, Nashwa Ahmad et al. / Short Term Electricity Load Forecasting Through Machine Learning. Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. editor / Aboul-Ella Hassanien ; Ahmad Taher Azar ; Tarek Gaber ; Diego Oliva ; Fahmy M. Tolba. Springer, 2020. pp. 427-437 (Advances in Intelligent Systems and Computing).

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title = "Short Term Electricity Load Forecasting Through Machine Learning",

abstract = "Essentially, Electricity Load Forecasting is an approximation of upcoming active loads from a variety of load buses before the active loads occur. Also, it is an important factor for power system energy management. Accurate and precise load forecasting can help to reduce the capacity of the power system, to make unit commitment decisions, and to increase the dependability of power systems. Hence, this paper presents a generalized regression Neural Network (GRNN) based approach for Short Term Load Forecasting (STLF). The results showed that the performance of GRNN with 30 neurons is better of short-term load forecasting in comparison with 10 neurons. For 10 neurons, the Mean Absolute Percentage Error (MAPE) was 2.10\% and Mean Absolute Error (MAE) was 306.21 MWh. However, for 30 neurons it was observed that MAPE is 1.81\% and MAE 268.48 MWh.",

keywords = "Electricity Load Forecasting, Energy management, Power system, Short Term Load Forecasting (STLF)",

author = "Azar, \{Ahmad Taher\} and Alaa Khamis and Kamal, \{Nashwa Ahmad\} and Brian Galli",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020 ; Conference date: 08-04-2020 Through 10-04-2020",

year = "2020",

doi = "10.1007/978-3-030-44289-7\_40",

language = "English",

isbn = "9783030442880",

series = "Advances in Intelligent Systems and Computing",

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pages = "427--437",

editor = "Aboul-Ella Hassanien and Azar, \{Ahmad Taher\} and Tarek Gaber and Diego Oliva and Tolba, \{Fahmy M.\}",

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Azar, AT, Khamis, A, Kamal, NA & Galli, B 2020, Short Term Electricity Load Forecasting Through Machine Learning. in A-E Hassanien, AT Azar, T Gaber, D Oliva & FM Tolba (eds), Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. Advances in Intelligent Systems and Computing, vol. 1153 AISC, Springer, pp. 427-437, 1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020, Cairo, Egypt, 8/04/20. https://doi.org/10.1007/978-3-030-44289-7_40

Short Term Electricity Load Forecasting Through Machine Learning. / Azar, Ahmad Taher; Khamis, Alaa; Kamal, Nashwa Ahmad et al.
Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. ed. / Aboul-Ella Hassanien; Ahmad Taher Azar; Tarek Gaber; Diego Oliva; Fahmy M. Tolba. Springer, 2020. p. 427-437 (Advances in Intelligent Systems and Computing; Vol. 1153 AISC).

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

TY - GEN

T1 - Short Term Electricity Load Forecasting Through Machine Learning

AU - Azar, Ahmad Taher

AU - Khamis, Alaa

AU - Kamal, Nashwa Ahmad

AU - Galli, Brian

PY - 2020

Y1 - 2020

N2 - Essentially, Electricity Load Forecasting is an approximation of upcoming active loads from a variety of load buses before the active loads occur. Also, it is an important factor for power system energy management. Accurate and precise load forecasting can help to reduce the capacity of the power system, to make unit commitment decisions, and to increase the dependability of power systems. Hence, this paper presents a generalized regression Neural Network (GRNN) based approach for Short Term Load Forecasting (STLF). The results showed that the performance of GRNN with 30 neurons is better of short-term load forecasting in comparison with 10 neurons. For 10 neurons, the Mean Absolute Percentage Error (MAPE) was 2.10% and Mean Absolute Error (MAE) was 306.21 MWh. However, for 30 neurons it was observed that MAPE is 1.81% and MAE 268.48 MWh.

AB - Essentially, Electricity Load Forecasting is an approximation of upcoming active loads from a variety of load buses before the active loads occur. Also, it is an important factor for power system energy management. Accurate and precise load forecasting can help to reduce the capacity of the power system, to make unit commitment decisions, and to increase the dependability of power systems. Hence, this paper presents a generalized regression Neural Network (GRNN) based approach for Short Term Load Forecasting (STLF). The results showed that the performance of GRNN with 30 neurons is better of short-term load forecasting in comparison with 10 neurons. For 10 neurons, the Mean Absolute Percentage Error (MAPE) was 2.10% and Mean Absolute Error (MAE) was 306.21 MWh. However, for 30 neurons it was observed that MAPE is 1.81% and MAE 268.48 MWh.

KW - Electricity Load Forecasting

KW - Energy management

KW - Power system

KW - Short Term Load Forecasting (STLF)

UR - https://www.scopus.com/pages/publications/85082994312

U2 - 10.1007/978-3-030-44289-7_40

DO - 10.1007/978-3-030-44289-7_40

M3 - Conference contribution

AN - SCOPUS:85082994312

SN - 9783030442880

T3 - Advances in Intelligent Systems and Computing

SP - 427

EP - 437

BT - Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020

A2 - Hassanien, Aboul-Ella

A2 - Azar, Ahmad Taher

A2 - Gaber, Tarek

A2 - Oliva, Diego

A2 - Tolba, Fahmy M.

PB - Springer

T2 - 1st International Conference on Artificial Intelligence and Computer Visions, AICV 2020

Y2 - 8 April 2020 through 10 April 2020

ER -

Azar AT, Khamis A, Kamal NA, Galli B. Short Term Electricity Load Forecasting Through Machine Learning. In Hassanien AE, Azar AT, Gaber T, Oliva D, Tolba FM, editors, Proceedings of the International Conference on Artificial Intelligence and Computer Visio, AICV 2020. Springer. 2020. p. 427-437. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-44289-7_40

Short Term Electricity Load Forecasting Through Machine Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this