Modeling short-term forecast of electricity consumption using artificial intelligence methods in Gilan province

Document Type : Original Article

Authors

1 Master of Science, Department of Renewable Energies and Environment, Faculty of New Sciences and Technologies, University of Tehran, Iran

2 Science and Technology Vice-Presidency, Tehran, Iran

10.22059/ses.2023.350868.1015

Abstract

Load forecasting is one of the most important aspects of the effective management of power systems and depends on various factors such as the means and sources of electricity production by each company, electric load demand, weather factors, economic factors and human activities.. The main goal of this article is the STLF in the regional electricity grid of Guilan province. In this research, after collecting data including wind speed, relative humidity, temperature, dew point, holidays, day length and the effect of corona disease, pre-processing is done on them and they are divided into five clusters using K_Means clustering algorithm. In the following, the relationships between the independent and dependent variables of electricity consumption in Guilan province, using linear regression (LR) algorithms, artificial neural network (ANN) and support vector regression (SVR) along with the Grid search optimization method, were investigated and analyzed in Python software and modeled in the Google Colab environment. The results show that the SVR algorithm has higher accuracy and longer implementation time than the two algorithms of ANN and LR.

Keywords

References

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Journal of sustainable Energy Systems
Volume 1, Issue 3
April 2022
Pages 209-230

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