Short‐term nodal load forecasting based on machine learning techniques. (17th August 2021)

• Record Type: Journal Article
• Authors: Lu, Dan; Zhao, Dongbo; Li, Zuyi
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-17
• Subjects: Bayesian Ridge Regression (BRR) -- General Regression Neural Network (GRNN) -- Least Absolute Shrinkage and Selection Operator (LASSO) -- Principal Component Analysis (PCA) -- Short‐term Nodal Load Forecasting (STNLF) -- Unit Commitment (UC); Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.13016
• Languages: English
• ISSNs: 2050-7038

Abstract

Abstract: This paper introduces an advanced Short‐term Nodal Load Forecasting (STNLF) method that forecasts nodal load profiles for the next day in power systems, based on the combined use of three machine learning techniques. Least Absolute Shrinkage and Selection Operator (LASSO) is employed to reduce the number of features for a single nodal load forecasting. Principal Component Analysis (PCA) is used to capture the features of historical loads in low‐dimensional space compared to the original high‐dimensional load space where features are barely possible to depict. Bayesian Ridge Regression (BRR) is utilized to decide the parameters of the prediction model from a statistics perspective. Tests based on modified PJM load data demonstrate the effectiveness of the proposed STNLF method compared to the state‐of‐the‐art General Regression Neural Network (GRNN) method. Moreover, the reliability of the day‐ahead Unit Commitment (UC) solution is shown to have been improved, based on the forecasted load data using the proposed STNLF method. Abstract : Machine learning techniques are employed to forecast nodal load by extracting patterns and their relationship with weather information into more condensed data.