Ultra-Short Term Load Forecasting

What is it about?

In order to deeply explore the influencing factors of load forecasting accuracy and improve the forecasting accuracy of ultra-short-term load forecasting, this paper proposes a method for analyzing the influencing factors of ultra-short-term load forecasting considering time series characteristics.

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Photo by Aleksandr Popov on Unsplash

Photo by Aleksandr Popov on Unsplash

Why is it important?

Based on the analysis of four different load characteristics, 8 kinds of time series characteristics such as the dispersion coefficient, slope and daily load rate of the daily load curve are extracted. And three kinds of load forecasting models are established, including autoregressive integrated moving average model (ARIMA), grey system and support vector machine (SVM), and then forecast the load in Anchorage, Alaska, USA. The effects of these 8-time series features on the prediction accuracy of the three load forecasting models are analyzed.

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