Improve the Accuracy of Short-term Forecasting Algorithms by Standardized Load Profile and Support Regression Vector: Case Study Vietnam

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ABSTRACT

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

KEYWORDS

Short-term load forecast; regression model, Standardized Load Profile; Support Vector Regression

Cite this paper

Nguyen Tuan Dung, Nguyen Thanh Phuong. (2019) Improve the Accuracy of Short-term Forecasting Algorithms by Standardized Load Profile and Support Regression Vector: Case Study Vietnam. International Journal of Control Systems and Robotics, 4, 58-64