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Long –term load forecasting of power systems using artificial neural network and ANFIS

Ammar, Naji and Sulaiman, Marizan and Mohamad Nor, Ahmad Fateh (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 18196608

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Artificial neural networks (ANN); neuro-fuzzy inference system (ANFIS); weather forecasting; electrical load
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001-1841 Production of electric energy or power. Powerplants. Central stations
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Power Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 30 Sep 2019 02:58
Last Modified: 30 Sep 2019 02:58
URI: http://eprints.uthm.edu.my/id/eprint/11535
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