179. SHORT-TERM LOAD FORECASTING USING TIME SERIES ANALYSIS: A CASE STUDY FOR THE REPUBLIC OF NORTH MACEDONIA

Ana Kotevska; Nevenka  Kiteva Rogleva

doi:10.51466/JEEIT2052135k

Ana Kotevska Faculty of Electical Enginnering and Information Technologies ,"Ss. Cyril and Methodius" University in Skopje , Rugjer Boskovic bb, P.O. box 574 , 1001 Skopje, Republic of Macedonia
Nevenka Kiteva Rogleva Faculty of Electical Enginnering and Information Technologies ,"Ss. Cyril and Methodius" University in Skopje , Rugjer Boskovic bb, P.O. box 574 , 1001 Skopje, Republic of Macedonia

DOI: https://doi.org/10.51466/JEEIT2052135k

Keywords: autoregressive integrated moving average (ARIMA), autocorrelation function (ACF), mean absolute percentage error (MAPE), partial autocorrelation function (PACF), seasonal autoregressive integrated moving average with explanatory variable (SARIMAX)

Abstract

Accurate load forecasting models are essential for normal operation and schedule planning in utility company. This paper presents the development of short-term load forecasting models for the Republic of North Macedonia and gives a comparison review of various models for load forecasting. These models use time series analysis such as the Autoregressive Integrated Moving Average model and the Seasonal Autoregressive Integrated Moving Average with Explanatory Variable model. Time series approach is one of the most used methods for short-term load forecasting. Models were designed and implemented in Python. The results were evaluated by the Mean Absolute Percentage Error of 0.5% for the forecasted day.