Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis
Load forecasting is very important for planning and operation in power system energy management. It reinforces the energy efficiency and reliability of power systems. Problems of power systems are tough to solve because power systems are huge complex graphically, widely distributed and influenced by...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Asian Research Publishing Network (ARPN)
2018
|
Subjects: | |
Online Access: | http://eprints.utem.edu.my/id/eprint/20814/2/marizan_80.pdf http://eprints.utem.edu.my/id/eprint/20814/ https://www.researchgate.net/publication/323470242_Long_-_Term_load_forecasting_of_power_systems_using_Artificial_Neural_Network_and_ANFIS http://eprints.utem.edu.my/20814/2/marizan_80.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Teknikal Malaysia Melaka |
Language: | English |
id |
my.utem.eprints.20814 |
---|---|
record_format |
eprints |
spelling |
my.utem.eprints.208142021-07-08T20:57:58Z http://eprints.utem.edu.my/id/eprint/20814/ Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis Sulaiman, Marizan Mohamad Nor, Ahmad Fateh Ammar, Naji T Technology (General) TA Engineering (General). Civil engineering (General) Load forecasting is very important for planning and operation in power system energy management. It reinforces the energy efficiency and reliability of power systems. Problems of power systems are tough to solve because power systems are huge complex graphically, widely distributed and influenced by many unexpected events. It has taken into consideration the various demographic factors like weather, climate, and variation of load demands. In this paper, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were used to analyse data collection obtained from the Metrological Department of Malaysia. The data sets cover a seven-year period (2009- 2016) on monthly basis. The ANN and ANFIS were used for long-term load forecasting. The performance evaluations of both models that were executed by showing that the results for ANFIS produced much more accurate results compared to ANN model. It also studied the effects of weather variables such as temperature, humidity, wind speed, rainfall, actual load and previous load on load forecasting. The simulation was carried out in the environment of MATLAB software. Asian Research Publishing Network (ARPN) 2018-02 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/20814/2/marizan_80.pdf Sulaiman, Marizan and Mohamad Nor, Ahmad Fateh and Ammar, Naji (2018) Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis. ARPN Journal Of Engineering And Applied Sciences, 13 (3). pp. 828-834. ISSN 1819-6608 https://www.researchgate.net/publication/323470242_Long_-_Term_load_forecasting_of_power_systems_using_Artificial_Neural_Network_and_ANFIS http://eprints.utem.edu.my/20814/2/marizan_80.pdf |
institution |
Universiti Teknikal Malaysia Melaka |
building |
UTEM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknikal Malaysia Melaka |
content_source |
UTEM Institutional Repository |
url_provider |
http://eprints.utem.edu.my/ |
language |
English |
topic |
T Technology (General) TA Engineering (General). Civil engineering (General) |
spellingShingle |
T Technology (General) TA Engineering (General). Civil engineering (General) Sulaiman, Marizan Mohamad Nor, Ahmad Fateh Ammar, Naji Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis |
description |
Load forecasting is very important for planning and operation in power system energy management. It reinforces the energy efficiency and reliability of power systems. Problems of power systems are tough to solve because power systems are huge complex graphically, widely distributed and influenced by many unexpected events. It has taken into consideration the various demographic factors like weather, climate, and variation of load demands. In this paper, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were used to analyse data collection obtained from the Metrological Department of Malaysia. The data sets cover a seven-year period (2009- 2016) on monthly basis. The ANN and ANFIS were used for long-term load forecasting. The performance evaluations of both models that were executed by showing that the results for ANFIS produced much more accurate results compared to ANN model. It also studied the effects of weather variables such as temperature, humidity, wind speed, rainfall, actual load and previous load on load forecasting. The simulation was carried out in the environment of MATLAB software. |
format |
Article |
author |
Sulaiman, Marizan Mohamad Nor, Ahmad Fateh Ammar, Naji |
author_facet |
Sulaiman, Marizan Mohamad Nor, Ahmad Fateh Ammar, Naji |
author_sort |
Sulaiman, Marizan |
title |
Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis |
title_short |
Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis |
title_full |
Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis |
title_fullStr |
Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis |
title_full_unstemmed |
Long –Term Load Forecasting Of Power Systems Using Artificial Neural Network And Anfis |
title_sort |
long –term load forecasting of power systems using artificial neural network and anfis |
publisher |
Asian Research Publishing Network (ARPN) |
publishDate |
2018 |
url |
http://eprints.utem.edu.my/id/eprint/20814/2/marizan_80.pdf http://eprints.utem.edu.my/id/eprint/20814/ https://www.researchgate.net/publication/323470242_Long_-_Term_load_forecasting_of_power_systems_using_Artificial_Neural_Network_and_ANFIS http://eprints.utem.edu.my/20814/2/marizan_80.pdf |
_version_ |
1705060035255599104 |