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