Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)

N. T. Dung, N. T. Phuong


Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.


short-term load forecast; regression model; standardized load profile; support vector regression

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