Long Term Load Forecasting Using Adaptive Neural Fuzzy Inference System: Case Study on Woldia Power Distribution System
Year: 2025
Type: article
Abstract: The basic important of planning and operation in power system energy management is load forecasting. In woldia there is expansion of residential, commercial and other service providing institutions. These institutions need electrical power supply for doing their day to day activities. As a result, there is a need of load forecasting to know the electrical load consumption and be ready to supply the customers of woldia city. Therefore, load forecasting strengthens the power systems' dependability and energy efficiency. Power systems are highly distributed, highly complex graphically, and subject to numerous unforeseen events, making power system problems difficult to solve. It has considered the different demographic aspects, such as population growth, time, historical load, economic growth of the city and current load of the distribution system. In this reserch, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has been used to determine the forecasted load using data collection obtained from woldia city and the results of the model has been summarized. The data sets cover a seven-year period (2016/17- 2022/23) on monthly basis and forecast for ten years (2023/24-2032/33). The performance evaluations of ANFIS model have been executed better when compared with other classical models. The performance specification of load forecasting MAPE and RMSE in ANFIS were improve by 2.0196% and 2.859984 respectively. The prediction performance of the ANFIS model for load forecasting based on the previous data trained was investigated and a good performance of future load forecasting has been recoded. It can be decided that the future load consumption of woldia city is directly related with the population growth and the GDP of the city.
DOI: 10.20372/nadre:10392
Authors: Getaye Yeshaneh, Hunachew Moges, Tesfahun Desale
Institution: Woldia University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6944741790
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