SHORT-TERM LOAD FORECASTING USING TIME SERIES ANALYSIS: A CASE STUDY FOR THE REPUBLIC OF NORTH MACEDONIA - Kotevska, Ana; Kiteva Rogleva, Nevenka (Journal of Electrical Engineering and Information Technologies, 2020)
Load forecasts are important for energy suppliers. Accurate models for load forecasting are essential to the operation and planning of a utility company. This paper involves the development of short-term load forecasting models for the Republic of North Macedonia and a comparison of various models. 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. The results were evaluated by the Mean Absolute Percentage Error of 0.5% for the forecasted day.
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).